Directory of all Master Modules
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Module Number INFO-4311 |
Module Title Modeling and Analysis of Embedded Systems |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | In the winter semester | |
Language of instruction | German | |
Type of Exam | Oral examination (written exam if there are a large number of participants), exercise points can included as a grade bonus in the assessment of the exam |
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Content | Embedded systems are a fundamental component in many technical systems and have become an integral part of everyday life, e.g. in mobile communications, medical technology, consumer electronics, the smart home, (fully) automated vehicles, industrial automation, and the Internet-of-Things (IoT). The associated extensive requirements for embedded systems with the manifold dependencies between software and hardware require application-specific design methods for embedded software. This module introduces modeling, analysis, and implementation techniques that consider the interaction of software with the underlying hardware architecture in terms of performance, energy efficiency, reliability, and functional safety at an early stage. Current research and development trends in embedded systems design are highlighted to introduce students to a topic of high industrial relevance at an early stage, providing both basic theoretical knowledge and domain-specific application skills. |
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Objectives | This module enables students to develop embedded systems and to compare specification techniques for embedded systems with each other and they are confronted with problems from the field of embedded systems that are relevant for science and industry. The students know the theoretical approaches to modelling and analysing embedded software, taking into account task scheduling, priority inversion, communication overhead as well as the influences of the hardware architecture, and can apply these to different practical problems in the design of embedded software systems. The exercises are worked on independently by the students in small groups and self-confidence, rhetorical skills and critical faculties are trained by demonstrating the achieved results. |
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Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Bringmann | |
Literature / Other | • O. Bringmann, W. Lange, M. Bogdan: Eingebettete Systeme: Entwurf, Modellierung und Synthese; De Gruyter Oldenbourg, 3. überarbeitete Auflage, 2018. |
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Last offered | Wintersemester 2022 | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, ML-CS |
Module Number INFO-4312 |
Module Title Design and Synthesis of Embedded Systems |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | In the summer semester | |
Language of instruction | German | |
Type of Exam | Oral examination (written exam if there are a large number of participants), successful exercises can result in a grade bonus |
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Content | Embedded systems have greatly influenced changes in data, communications, and automotive technologies over the past decade and have become an integral part of everyday life. This module covers the specific hardware aspects of Embedded Systems. Topics include: IC technologies for Embedded Systems, hardware design methods, modeling concepts and simulation methods using hardware description languages. Furthermore, communication between processes and modules within a chip is covered, different synchronization types are shown and on-chip bus systems from practice are presented. A main focus is on automated circuit synthesis from hardware and system description languages (VHDL, Verilog and SystemC) or software programming languages (C/C++). First, register transfer synthesis, logic synthesis, and technology mapping are addressed. Then, the concepts of architecture synthesis (high level synthesis) are introduced with the basic algorithms for clock cycle accurate scheduling and resource commitment. With the help of hardware description languages such as VHDL and Verilog, methods for modeling and simulating embedded systems are taught, which are applied and deepened accordingly through independent work in the exercises. |
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Objectives | Students will acquire specialist competences as well as basic concepts and technologies of modern embedded systems. The students are enabled to know and apply the principles of relevant hardware basic technologies, to master development techniques theoretically and practically and to be able to assess and optimise embedded systems. Furthermore, the students acquire an understanding of the methods and concepts of hardware description languages and automated circuit synthesis in digital hardware design. The exercises are worked on independently by the students in small groups and self-confidence, rhetorical skills and critical faculties are trained by demonstrating the results achieved. |
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Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Bringmann | |
Literature / Other | • O. Bringmann, W. Lange, M. Bogdan: Eingebettete Systeme: Entwurf, Synthese und Edge AI; De Gruyter Oldenbourg, 4. überarbeitete Auflage, 2022. • J. Teich, C. Haubelt: Digitale Hardware/Software-Systeme: Synthese und • G. De Micheli: Synthesis and Optimization of Digital Circuits. McGraw- |
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Last offered | Sommersemester 2022 | |
Planned for | Sommersemester 2023 | |
Assigned Study Areas | INFO-INFO, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, ML-CS |
Module Number INFO-4313 |
Module Title Embedded Systems |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | In the summer semester | |
Language of instruction | English | |
Type of Exam | Presentation and written report |
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Content | Embedded systems have become a fundamental part of many technical systems |
|
Objectives | Students are able to read, reflect, and examine the topic in substance upon current |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Bringmann | |
Literature / Other | Will be announced in the pre-lecture meeting. |
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Last offered | Sommersemester 2022 | |
Planned for | Sommersemester 2024 | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, MEDZ-SEM, ML-CS |
Module Number INFO-4315 |
Module Title Advanced Topics in Embedded Systems |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Oral examination (written exam if there are a large number of participants) |
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Content | This lecture discusses current topics and trends in embedded system research |
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Objectives | Participants will acquire in-depth knowledge to different aspects in embedded |
|
Prerequisite for participation |
INFO-4311 Modeling and Analysis of Embedded Systems, INFO-4312 Design and Synthesis of Embedded Systems |
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Lecturer | Bringmann | |
Literature / Other | Will be announced during the first lecture. |
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Last offered | Sommersemester 2020 | |
Planned for | --- | |
Assigned Study Areas | INFO-INFO, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, ML-CS |
Module Number INFO-4316 |
Module Title Programming Ultra-Low Power Architectures |
Lecture Type(s) Practical Course |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | In the summer semester | |
Language of instruction | German | |
Type of Exam | Elaboration and presentation of the internship tasks |
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Content | This module provides an introduction to practical work with microcontrollers. The FRDM-KL25Z development platform based on a 32-bit ARM Cortex M0+ processor is used for this purpose. After a short introduction to the platform used, practical tasks are solved in teams of two. The practical tasks cover the following topics: Introduction to microcontroller programming, application execution time, performance analysis and optimization and memory requirements. |
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Objectives | The students can systematically develop software for embedded systems taking into account electrical power consumption and energy consumption. They know the entire development process from specification, through development, to debugging and documentation. Furthermore, the students are able to apply modern techniques of software-supported dynamic power management up to the programming of ultra-low-power applications. Emphasis is placed on teamwork, communication within and between groups, systematic problem solving and meeting deadlines. This promotes students' self-confidence, self-marketing skills and ability to deal with conflicts. |
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Prerequisite for participation | INFO-4311 Modeling and Analysis of Embedded Systems | |
Lecturer | Bringmann | |
Literature / Other | Literatur wird zu Beginn des Praktikums bekanntgegeben. |
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Last offered | Sommersemester 2022 | |
Planned for | Sommersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-TECH, MEDI-APPL, MEDI-INFO, ML-CS |
Module Number INFO-4317 |
Module Title Parallel Computer Architectures |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | In the winter semester | |
Language of instruction | German | |
Type of Exam | Mündliche Prüfung (bei großer Teilnehmerzahl Klausur), durch erfolgreiche Übungen kann ein Notenbonus erarbeitet werden. |
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Content | The module deals with the topic of parallel data processing from the perspective of computer architecture. Computer architecture concepts are presented, with the help of which parallelism can be exploited at various levels to increase performance. The module covers the following topics, among others: Machine instruction-level parallelism: superscalar technique, speculative execution, jump prediction, VLIW principle, multi-threaded instruction execution. Modern parallel computing concepts, memory-coupled parallel computers, symmetric multiprocessors, distributed shared memory multiprocessors, message-oriented parallel computers, multicore architectures, cache coherence protocols, performance evaluation of parallel computing systems, parallel programming models, interconnection networks (topology, routing), heterogeneous system architectures and GPGPUs. |
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Objectives | The students have extended technical competences in the area of modern computer architectures with a focus on parallel architectures, interconnection networks and heterogeneous systems. They know the advantages and disadvantages of the various parallel architectures as well as the difficulties that arise when programming such systems. This enables the students to apply appropriate programming concepts for parallel architectures in a situation-appropriate manner. In the exercises, the participants acquire a further understanding of the complexity of parallel processes and the resulting difficulties. Due to the independent work in small groups, the ability to work in a team and leadership qualities are particularly promoted. |
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Prerequisite for participation | INF3341 Introduction to Computer Architecture | |
Lecturer | Bringmann | |
Literature / Other | • J. L. Hennessy, D. A. Patterson: Computer Architecture: A Quantitive Approach, Morgan Kaufmann Publishers Inc, Elsevier, 6. Auflage, 2018. |
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Last offered | Wintersemester 2022 | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, ML-CS |
Module Number INFO-4318 |
Module Title Advanced Computer Architecture |
Lecture Type(s) Practical Course |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German | |
Type of Exam | Elaboration and presentation of the internship tasks |
|
Content | The practical course deals with current research questions in computer architecture by means of practical tasks. This includes assignments on the following topics: Simulative Evaluation of Computer Architectures, Microarchitecture and Instruction Sets, Jump Prediction and Speculative Execution, Caches and Cache Coherence, Memory Organization and Interconnection Networks, and Computer Architecture Support for Operating Systems. In addition, an independently developed research project concludes of the practical course. |
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Objectives | This practical course enables students to evaluate current research questions in the field of computer architecture and to work on new questions independently. Through the practical handling of parallel architectures and parallelised applications, the students gain a further understanding of the complexity of parallel processes and the resulting difficulties. The independent processing of tasks enables the students to deal with methods and tools relevant to everyday life in science and business. Furthermore, they acquire the competence to program parallel computers efficiently and to apply the learned skills in depth within the framework of a research project. The tasks set in this module are worked on in small groups. In addition to teamwork, communication and conflict skills, this also trains the students' sense of responsibility. |
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Prerequisite for participation | INFO-4317 Parallel Computer Architectures | |
Lecturer | Bringmann | |
Literature / Other | • J. L. Hennessy, D. A. Patterson: Computer Architecture: A Quantitive Approach, Morgan Kaufmann Publishers Inc, Elsevier, 6. Auflage, 2018. |
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Last offered | --- | |
Planned for | --- | |
Assigned Study Areas | INFO-INFO, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, ML-CS |
Module Number INFO-4194 |
Module Title Behavior and Learning |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Written exam (in case of a small number of participants: oral tests) |
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Content | This lecture builds on the available knowledge how animals and humans plan, |
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Objectives | Students know how intelligent behavior can be generated and learned in artificial |
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Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Butz | |
Literature / Other | Literatur / Literature: Voraussetzungen / Prerequisites: |
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Last offered | --- | |
Planned for | --- | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-HCI, MEDI-INFO, MEDI-MEDI, ML-CS |
Module Number INFO-4210 |
Module Title Recurrent and Generative Artificial Neural Networks |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Written exam (in case of a small number of participants: oral tests) |
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Content | Advanced ANN topics. First, revisiting backpropagation and backpropagation |
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Objectives | Students know about and how to apply generative and typically recurrent artificial |
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Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Butz | |
Literature / Other | Literatur / Literature: Voraussetzungen / Prerequisites: |
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Last offered | Sommersemester 2022 | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-VIS, ML-CS, ML-DIV |
Module Number INFO-4211 |
Module Title Avatars in Virtual Realities |
Lecture Type(s) Practical Course |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Final Project Presentation and Report |
|
Content | In this project-oriented practical course, students learn how to design realistic, |
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Objectives | Students know how to work with virtual realities (VRs) and how to develop |
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Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Butz | |
Literature / Other | keine / Solid Knowledge in Programming. General knowledge about simulation software. |
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Last offered | - | |
Planned for | - | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-HCI, MEDI-INFO, MEDI-MEDI, MEDI-MMT, MEDI-PRAX, ML-CS |
Module Number INFO-4212 |
Module Title Artificial Neural networks |
Lecture Type(s) Practical Course |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Final Project Presentation and Report |
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Content | Programming enhanced functionalities in ANN Software, evaluating performance, |
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Objectives | Know how to work with, implement, and enhance complex artificial neural |
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Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Butz | |
Literature / Other | keine / Solid Knowledge in Programming. Knowledge about artificial neural networks |
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Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-PRAX, MEDI-VIS, ML-CS |
Module Number INFO-4213 |
Module Title Advanced Artificial Neural Networks Project |
Lecture Type(s) Practical Course |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Final Project Presentation and Report |
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Content | Working with ANN Software, evaluating performance, & analyzing the system. |
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Objectives | Know how to evaluate, program, and analyze artificial neural networks. |
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Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Butz | |
Literature / Other | keine / Solid Knowledge in Programming. Knowledge about artificial neural networks |
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Last offered | - | |
Planned for | - | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-VIS, ML-CS, ML-DIV |
Module Number INFO-4214 (MKOGP3) |
Module Title Cognitive Modeling |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | In the summer semester | |
Language of instruction | English | |
Type of Exam | Written exam (in case of a small number of participants: oral tests) |
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Content | Cognitive models covering learning, action and perception are presented and |
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Objectives | Students know the most important principles and techniques of cognitive modeling. |
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Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Butz, Wichmann | |
Literature / Other | Book: S. Lewandowsky & S. Farrell (2011). Computational Modeling in Cognition. |
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Last offered | Sommersemester 2022 | |
Planned for | Sommersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-HCI, MEDI-INFO, MEDI-MEDI, ML-CS |
Module Number BIOINF4998 (entspricht BIO-4998) |
Module Title Research Project Bioinformatics |
Lecture Type(s) Research Project |
---|---|---|
ECTS | 9 | |
Work load - Contact time - Self study |
Work load:
270 h Class time:
90 h / 6 SWS Self study:
180 h |
|
Duration | 1 Semester | |
Frequency | Each semester | |
Language of instruction | German and English | |
Type of Exam | Presentation and written report (either as a scientific paper or as a report (15-20 pages) |
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Content | The research project aims to deepen theoretical and practical knowledge in a |
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Objectives | Bioinformatics research project: The students |
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Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Dozenten der Bioinformatik | |
Literature / Other | Wissenschaftliche Literatur/Veröffentlichungen relevant für das zu bearbeitende Forschungsthema / Exzellente akademische Noten im Master Bioinformatik. Es gibt nur wenige Forschungsprojekte, die semesterweise angeboten werden. Eine schriftliche Bewerbung, incl. Motivationsschreiben, CV und Transcript of Records sind an den Arbeitsgruppenleiter des angebotenen Forschungsprojektes zu schicken. |
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Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | BIO-BIO |
Module Number INFO-4998 |
Module Title Research Project Computer Science |
Lecture Type(s) Research Project |
---|---|---|
ECTS | 9 | |
Work load - Contact time - Self study |
Work load:
270 h Class time:
90 h / 6 SWS Self study:
180 h |
|
Duration | 1 Semester | |
Frequency | Each semester | |
Language of instruction | German and English | |
Type of Exam | Presentation and written report |
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Content | The research project serves to deepen theoretical and practical knowledge in a specific area of practical / theoretical / technical computer science. Students work on a research project of the thematic focus of the research group and are ideally involved in the production of a scientific publication in the topic area. |
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Objectives | The students |
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Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Dozenten der Informatik | |
Literature / Other | Wissenschaftliche Literatur/relevante Veröffentlichungen für das zu bearbeitende Forschungsthema. |
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Last offered | --- | |
Planned for | --- | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, ML-CS |
Module Number MEDI-4998 |
Module Title Research Project Media Informatics |
Lecture Type(s) Research Project |
---|---|---|
ECTS | 9 | |
Work load - Contact time - Self study |
Work load:
270 h Class time:
90 h / 6 SWS Self study:
180 h |
|
Duration | 1 Semester | |
Frequency | Each semester | |
Language of instruction | German and English | |
Type of Exam | Written elaboration and presentation based on it |
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Content | The research project serves to deepen theoretical and practical knowledge in a specific area of media informatics. Students work on a research project of the thematic focus of the research group and are preferably involved in the production of a scientific publication in the thematic area. |
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Objectives | Research project in media informatics: The students |
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Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Dozenten der Medieninformatik | |
Literature / Other | Wissenschaftliche Literatur/Veröffentlichungen relevant für das zu bearbeitende Forschungsthema |
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Last offered | --- | |
Planned for | --- | |
Assigned Study Areas | MEDI-PRAX |
Module Number MEDZ-4998 |
Module Title Research Project Medical Informatics |
Lecture Type(s) Research Project |
---|---|---|
ECTS | 9 | |
Work load - Contact time - Self study |
Work load:
270 h Class time:
90 h / 6 SWS Self study:
180 h |
|
Duration | 1 Semester | |
Frequency | Each semester | |
Language of instruction | German and English | |
Type of Exam | Written elaboration and presentation based on it |
|
Content | The research project serves to deepen theoretical and practical knowledge in a specific area of medical informatics. Students work on a research project with the thematic focus of the research group. Students acquire knowledge in the current research environment of medical informatics and in independent research including the necessary documentation and handling of primary research data. Thus, the course leads directly to a research-related master thesis. |
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Objectives | The students |
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Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Dozenten der Medizin- und Bioinformatik | |
Literature / Other | Wissenschaftliche Literatur/Veröffentlichungen relevant für das zu bearbeitende Forschungsthema / Exzellente akademische Noten im Master Medizininformatik. Es gibt nur wenige Forschungsprojekte, die semesterweise angeboten werden. Eine schriftliche Bewerbung, incl. Motivationsschreiben, CV und Transcript of Records sind an die Arbeitsgruppenleiter*in des angebotenen Forschungsprojektes zu schicken. |
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Last offered | --- | |
Planned for | --- | |
Assigned Study Areas | MEDZ-RES |
Module Number INFO-4413 |
Module Title Parameterized Algorithms |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German | |
Type of Exam | Written exam (in case of a small number of participants: oral tests) |
|
Content | This module gives an introduction to the theory of parameterized algorithms and parameterized complexity theory. The focus is on different methods and techniques for developing parameterized algorithms. This module introduces solution approaches to NP complete problems, parameterized algorithms, and problem kernels. Different methods and techniques, such as data reduction and problem kernels, depth-constrained search trees, dynamic programming, tree decompositions, iterative compression, color coding, and linear programming, are introduced. From the area of parametrized complexity theory, parametrized reduction, the class FPT and Hardness classes are treated. |
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Objectives | Students have basic knowledge of parameterised algorithms and parameterised complexity and can assess and determine the difficulty of NP-complete problems and algorithms for solving them exactly. They know different methods and techniques for designing parameterised algorithms. They can solve different problems with the repertoire of methods presented as well as creatively develop parameterised algorithms independently. The students can distinguish between different strategies for the design of parameterised algorithms and apply these adapted to the problem. The students are able to critically evaluate a parameterised algorithm. They recognise advantages and disadvantages of this approach and can place it in the context of other methods for solving NP-complete problems such as heuristics, approximation algorithms, randomised algorithms. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Dorn | |
Literature / Other | Rolf Niedermeier: Invitation to Fixed-Parameter Algorithms, Oxford University Press. |
|
Last offered | vor Sommersemester 2020 | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS |
Module Number INFO-4414 |
Module Title Parameterized Algorithms |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German and English | |
Type of Exam | To be announced. |
|
Content | The seminar includes the elaboration of written sources on topics from the field of Parametrized Algorithms and Complexity under supervision. Presentation and the written summary conclude the seminar work in each case. Active participation in each session is an important part of the seminar. |
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Objectives | The students can independently work out and understand an extended and complex subject from the field of Parametrised Algorithms and Parametrised Complexity from a written source and present it in the form of a lecture and also represent it in a discussion in front of a plenum. In addition to the oral presentation, they can present and summarise the elaborated topic in writing. |
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Prerequisite for participation | INFO-4413 Parameterized Algorithms | |
Lecturer | Dorn | |
Literature / Other | Rolf Niedermeier: Invitation to Fixed-Parameter Algorithms, Oxford University Press, und Weitere (wechselnd). |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS |
Module Number INFO-4417 |
Module Title Parameterized Algorithms and Complexity |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 9 | |
Work load - Contact time - Self study |
Work load:
270 h Class time:
90 h / 6 SWS Self study:
180 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German | |
Type of Exam | Written exam (in case of a small number of participants: oral tests) |
|
Content | This module gives an extended introduction to the theory of parameterized algorithms and parameterized complexity theory. The focus is on different methods and techniques for developing parameterized algorithms. This module introduces solution approaches to NP-complete problems, parameterized algorithms, and problem kernels. Different methods and techniques, such as data reduction and problem kernels, depth-constrained search trees, dynamic programming, tree decompositions, iterative compression, color coding, and linear programming, are introduced. From the area of parametrized complexity theory, parametrized reduction, the class FPT and hardness classes are treated. In addition, issues and trends of current research as well as applications in various fields such as bioinformatics, artificial intelligence, or Computational Social Choice are presented. |
|
Objectives | Students have basic knowledge of parameterised algorithms and parameterised complexity and can assess and determine the difficulty of NP-complete problems and algorithms for solving them exactly. They know different methods and techniques for designing parameterised algorithms. They can solve different problems with the repertoire of methods presented as well as creatively develop parameterised algorithms independently. The students can distinguish between different strategies for the design of parameterised algorithms and apply these adapted to the problem. They are able to critically evaluate a parameterised algorithm. They recognise advantages and disadvantages of this approach and can classify it in the context of other methods for solving NP-complete problems such as heuristics, approximation algorithms, randomised algorithms. In addition, the students are familiar with issues of current research and know application examples and case studies. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Dorn | |
Literature / Other | Rolf Niedermeier: Invitation to Fixed-Parameter Algorithms, Oxford University Press, 2006. |
|
Last offered | --- | |
Planned for | --- | |
Assigned Study Areas | INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS |
Module Number INFO-4999 |
Module Title Seminar on Selected Topics in Practical Computer Science |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Presentation and written report |
|
Content | In this module we discuss advanced topics in the area of practical computer science. Note on the winter term 2024: |
|
Objectives | Students get to know current topics in the area of practical computer science. Students will be able to acquire knowledge about state-of-the-art topics in practical computer science through comprehensive literature search. Students will not only have improved their study and reading skills, but will also have enhanced their capability of working independently. The teaching method in this seminar aims at boosting the students’ confidence (oral presentation), and at enhancing their communication skills and enabling them to accept criticism (discussion session following their presentation. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Alle Dozenten | |
Literature / Other | Will be handed out in the course |
|
Last offered | unknown | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS |
Module Number MEDZ-4310 |
Module Title Selected Topics in Medical Informatics |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Oral examination (written exam if there are a large number of participants) |
|
Content | The course deepens the crossover between medicine and bioinformatics and forms a core course in the research-oriented MSc Medical Informatics. |
|
Objectives | The students recognise the connection between medicine and bioinformatics on the basis of current research fields. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Dozenten der Medizin- und Bioinformatik | |
Literature / Other | - |
|
Last offered | --- | |
Planned for | --- | |
Assigned Study Areas | MEDZ-BIOMED, MEDZ-RES |
Module Number MEDZ-4320 |
Module Title Selected Topics in Medical Informatics |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 9 | |
Work load - Contact time - Self study |
Work load:
270 h Class time:
90 h / 6 SWS Self study:
180 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Oral Test |
|
Content | The course deepens the crossover between medicine and bioinformatics and forms a core course in the research-oriented MSc Medical Informatics. |
|
Objectives | Based on current research fields, students recognise the connection between medical informatics and bioinformatics. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Dozenten der Medizin- und Bioinformatik | |
Literature / Other | - |
|
Last offered | --- | |
Planned for | --- | |
Assigned Study Areas | MEDZ-BIOMED, MEDZ-RES |
Module Number BIOINF4393 (entspricht BIO-4393) |
Module Title Mathematical Methods in (Medical) Systems Biology |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Written elaboration and presentation based on it |
|
Content | This seminar focuses on the key concepts of computational, mathematical, and statistical models used in cancer and cancer medicine, and helps students learn how mathematical models help us understand cancer progression. The technical articles used in this seminar provide the biological background and describe the development of both classical mathematical models and more recent representations of biological processes. Therefore, students will examine existing mathematical models and learn to deal with the key parameters involved in modeling and the impact of changes in these parameters to discuss how they relate to treatment, prevention, or policy making in general. Through discussions of published work, students will learn how to critically evaluate a modeling effort and how to communicate modeling results to readers of scientific journals. The seminar is useful for students who use experimental techniques as an approach in the laboratory and want to use computational modeling as a tool to gain a deeper understanding of experiments. |
|
Objectives | Students learn to apply methods of mathematical modelling to systems biology models. This includes |
|
Prerequisite for participation |
INFM1010 Mathematics for Computer Science 1: Analysis, INFM1020 Mathematics for Computer Science 2: Linear Algebra, INFM2010 Mathematics for Computer Science 3: Advanced Topics |
|
Lecturer | Mostolizadeh | |
Literature / Other | Originalarbeiten und zusätzliche Materialien werden im Seminar ausgegeben. |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | BIO-BIO, BIO-SEM, INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-MEDTECH, MEDZ-SEM, ML-CS |
Module Number BIOINF4394 (entspricht BIO-4394) |
Module Title Systems Biology II |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | In the summer semester | |
Language of instruction | English | |
Type of Exam | Written exam (oral exam with a small number of participants), practice certificate as an exam requirement. Practice points can be included as bonus points in the exam evaluation. |
|
Content | This application-oriented course imparts essential knowledge on the dynamic modeling of biological systems. This opens up numerous application areas, such as optimizing biotechnological processes, personalized medicine, preclinical studies, and understanding current systems biology research. In addition, students learn to work with the programming environment Tellurium, which is based on the Python programming language and brings with it the declarative systems biology modeling language called “Antimony.” Students will learn the basic approach to building biochemical reaction models and concepts for analyzing dynamic network states. Data sources and forms of representation for the models will be covered. Emphasis is placed on physical constraints and implicit assumptions, such as conservation of mass, types of biochemical reactions, principles of enzyme catalysis, application and derivation of kinetic equations, open and closed systems and the influence of reversible reactions on the overall system, and processes occurring on different time scales to obtain plausible models. Furthermore, energy conservation, the influence of cofactors and redox potentials, and regulatory mechanisms in biochemical systems are considered. Students learn how to classify the correctness of simulation results by estimating the magnitudes of cellular components. Students will gain an overview of numerical methods relevant to simulation and learn how to simulate models dynamically. Suitable graphical representations for the analysis of simulation results are discussed. Finally, the principles learned are applied to selected metabolic pathways, and their coupling concerning the cellular scale is discussed. The content does not build directly on the lecture Systems Biology I so this course can be attended independently. |
|
Objectives | Students learn to apply methods of mathematical modeling to systems biology models. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Dräger | |
Literature / Other | 1. Bernhard Ø. Palsson 2011. Systems Biology: Simulation of Dynamic Network |
|
Last offered | Sommersemester 2022 | |
Planned for | Wintersemester 2023 | |
Assigned Study Areas | BIO-BIO, MEDZ-BIOMED, MEDZ-RES |
Module Number INFO-4250 |
Module Title Information Processing for Perception and Action |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German and English | |
Type of Exam | Will be announced at beginning of semester |
|
Content | Humans as well as complex technical systems process sensory information to |
|
Objectives | Students will know current views on biological information processing and on |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Franz | |
Literature / Other | Wird zu Beginn des Semesters bekanntgegeben / Will be announced at beginning |
|
Last offered | - | |
Planned for | - | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-HCI, MEDI-INFO, MEDI-MEDI, MEDZ-SEM, ML-CS |
Module Number INFO-4177 |
Module Title Intelligent Systems II - Learning in Computer Vision |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Written exam (in case of a small number of participants: oral tests) |
|
Content | Graphical Models; Bayesian Belief Networks; Markov Random Fields; Conditional Random Fields; Learning of Structured Variables; Bayesian Decision Theory; Loss-based Learning; Parameter Learning in Graphical Models; Structured Support Vector Machines; Exact and Approximate Inference Methods; Applications in Image Processing; Segmentation; Human Pose Estimation; Image Denoising; Stereo; Object Detection. |
|
Objectives | Students learn how complicated statistical relationships can be represented with the help of graphical models. Concrete and current problems from the fields of image processing and image understanding are solved. Various learning methods make it possible to automatically set data-driven parameters and evaluate the performance achieved. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Gehler, Lensch, MPI | |
Literature / Other | Vorlesungsfolien werden bereitgestellt |
|
Last offered | Sommersemester 2020 | |
Planned for | --- | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-VIS, ML-CS, ML-DIV |
Module Number INFO-4141 |
Module Title Implementation of Relational Database Systems (DB2) |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 9 | |
Work load - Contact time - Self study |
Work load:
270 h Class time:
90 h / 6 SWS Self study:
180 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German and English | |
Type of Exam | Written exam (oral exam with a small number of participants), exercise points |
|
Content | Internals of PostgreSQL and MonetDB, secondary storage access and data layout, index structures (B+ trees, hashes), multidimensional index structures, sorting methods on secondary storage, query evaluation, plan generation and optimization for SQL, transactions (ACID principle). |
|
Objectives | The role and relevance of the internals of a database system are clarified and analysed. Throughout the semester, we contrast PostgreSQL and MonetDB so that students can assess the suitability of disk-based and main memory-based database technology for concrete applications. Students know how to link this new knowledge with the concepts of the lecture "DB1". The students understand which basic parameters and algorithms enable efficient database operation and can optimise them for concrete applications. In doing so, the topic is treated in a depth that provides students with reading and learning skills as well as trains discipline and precision. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Grust | |
Literature / Other | • Ramakrishnan / Gehrke: Database Management Systems |
|
Last offered | Sommersemester 2022 | |
Planned for | Sommersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, ML-CS |
Module Number INFO-4142 |
Module Title Database Systems and Modern CPU Architecture |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German and English | |
Type of Exam | Written exam (oral exam with a small number of participants), exercise points |
|
Content | CPU architectures, pipelining, parallelism, multi-scale CPUs, pipelining and query evaluation, CPU caches, cache aware database architecture, main memory databases (internals and practical use). |
|
Objectives | Students understand a database system as a synthesis of CPU/computer architecture and actual database architecture. They can evaluate existing database architectures with regard to their suitability for execution on a given computer architecture. This module connects the worlds of CPUs (instruction level) and database systems (query processor) and thus promotes system understanding across many architectural levels. |
|
Prerequisite for participation | INFO-4141 Implementation of Relational Database Systems (DB2) | |
Lecturer | Grust | |
Literature / Other | • Hennessy / Patterson: Computer Architecture - A Quantitative Approach |
|
Last offered | --- | |
Planned for | --- | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, ML-CS |
Module Number INFO-4147 |
Module Title Declarative Database Languages |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German and English | |
Type of Exam | Written exam (oral exam with a small number of participants), exercise points |
|
Content | Semantics and internal representation of SQL (e.g., comprehensions), compilation of SQL, database languages for non-relational data, new paradigms for data-intensive programming, interaction of databases and programming environments, compilation of programming language constructs for execution on database systems |
|
Objectives | The students know compilation techniques for the database languages covered. References to classical compiler construction and the necessity of new translation methods are recognised. The students know the central concept of impedance mismatch, which determines the entire subject area. The resulting problems are analysed and alternative solutions can be assessed in terms of usability and efficiency. References to functional programming languages (semantics and translation methods) can be recognised and exploited. The topic is treated in a depth that provides the students with reading and learning skills and trains discipline and precision. |
|
Prerequisite for participation |
INF3131 Introduction to Relational Database Systems (DB1), INF3182 Compiler Construction |
|
Lecturer | Grust | |
Literature / Other | • Compiler / Interpreter und Datenbanksysteme (Software und Manuals) |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, ML-CS |
Module Number INFO-4149 |
Module Title Selected Topics in Database Systems |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German and English | |
Type of Exam | Written exam (oral exam with a small number of participants), exercise points |
|
Content | Changing in-depth topics from the subfields of the research field of database systems. Use of database systems for the realization of demanding applications (Advanced SQL). |
|
Objectives | Students have knowledge of research methodology in the field of database systems. The focus is primarily on the use of SQL as a database language, its efficient translation, as well as its use for the realisation of very complex applications. The participants are prepared for writing scientific papers, especially in sub-areas of the research field of database systems. The students can prepare specifically for Master's theses and research projects. |
|
Prerequisite for participation | INF3131 Introduction to Relational Database Systems (DB1) | |
Lecturer | Grust | |
Literature / Other | Klassische und aktuelle Forschungsliteratur zum Themengebiet. |
|
Last offered | unknown | |
Planned for | Sommersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, ML-CS |
Module Number INFO-4663 |
Module Title Advanced Topics in Database Systems |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Presentation |
|
Content | The seminar includes the elaboration of sources under supervision on advanced topics in the field of database technology. The presentation and the written summary conclude the seminar work in each case. Active participation in the individual sessions is an important part of the seminar. Note on the winter semester 2024: |
|
Objectives | The students can independently work out and understand an extended and complex issue in the field of database systems from original (English) sources and present it in the form of a presentation and also represent it in a discussion in front of a plenum. The use of different presentation techniques is weighed up, trained and practised in the plenary. As listeners, the participants are able to give their fellow students critical but fair feedback on the content and formal aspects of the presentation. In addition to the oral presentation, they can present the developed topic in writing and summarise it in the form of a short scientific article. |
|
Prerequisite for participation | INF3131 Introduction to Relational Database Systems (DB1) | |
Lecturer | Grust | |
Literature / Other | Wechselnde Original-Literatur aus dem Forschungsfeld der Datenbanksysteme |
|
Last offered | Wintersemester 2022 | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS |
Module Number INFO-4664 |
Module Title Data and Business Analytics |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German | |
Type of Exam | Presentation 40%, elaboration 40%, participation in the discussions 20% |
|
Content | The seminar covers theoretical foundations as well as practical and work-related implementation of Real-World applications in science and economics. The selection of the specific topics depends on the interests and knowledge of the students. The topics will be distributed in a preliminary meeting (in case of two many interests they will be drawn). |
|
Objectives | TBD |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Huber | |
Literature / Other | - |
|
Last offered | Sommersemester 2021 | |
Planned for | Sommersemester 2023 | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-HCI, MEDI-INFO, MEDI-MEDI, MEDZ-SEM, ML-CS |
Module Number BIOINF4241 (entspricht BIO-4241) |
Module Title App Design in Bioinformatics |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Written Test |
|
Content | inhalt 4241 |
|
Objectives | goals 4241 |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Huson | |
Literature / Other | - |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | BIO-BIO, INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-HCI, MEDI-INFO, MEDI-MEDI, MEDZ-BIOMED, ML-CS |
Module Number BIOINF4242 (entspricht BIO-4242) |
Module Title Advanced Topics in Bioinformatics |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Each semester | |
Language of instruction | English | |
Type of Exam | Programming project |
|
Content | In this course, we study the latest features of Java to address challenging programming |
|
Objectives | The students are able to design and implement a fully featured bioinformatics |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Huson | |
Literature / Other | Programming and bioinformatics literature |
|
Last offered | Sommersemester 2022 | |
Planned for | Sommersemester 2023 | |
Assigned Study Areas | BIO-BIO, INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDZ-BIOMED, ML-CS |
Module Number BIOINF4240 (entspricht BIO-4240) |
Module Title Bioinformatics Tools |
Lecture Type(s) Practical Course |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Each semester | |
Language of instruction | English | |
Type of Exam | The final grade is based on performance, a written report on each day of the practical course, and one or two short oral presentations. |
|
Content | In this practical course, students work on a mini research project in the area of |
|
Objectives | Students will gain practical experience in application of bioinformatics software |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Huson | |
Literature / Other | Scientific publications |
|
Last offered | Sommersemester 2022 | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | BIO-PRAK |
Module Number BIOINF4311 (entspricht BIO-4311) |
Module Title Microbiome Analysis |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Written or oral exam |
|
Content | This course provides an in-depth introduction to microbiome analysis. Topics |
|
Objectives | The students are familiar with recent bioinformatics findings on microbiome |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Huson | |
Literature / Other | Lecture notes and scientific publications |
|
Last offered | Sommersemester 2021 | |
Planned for | currently not planned | |
Assigned Study Areas | BIO-BIO, INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDZ-BIOMED, ML-CS |
Module Number BIOINF4322 (entspricht BIO-4322) |
Module Title Metagenomics |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Oral Presentation (about 30 minutes) and written elaboration (approx. 10 pages), leading the discussion once |
|
Content | In this seminar, we look at current research topics in the area of microbiome |
|
Objectives | The students can independently work with supervision on a challenging topic |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Huson | |
Literature / Other | Scientific publications |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | BIO-BIO, BIO-SEM, MEDZ-BIOINFO, MEDZ-SEM |
Module Number BIOINF4361 (entspricht BIO-4361) |
Module Title Advanced Sequence Analysis |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | In the winter semester | |
Language of instruction | English | |
Type of Exam | Written Test |
|
Content | inhalt bio4361 |
|
Objectives | goals bio 4361 |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Huson | |
Literature / Other | - |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | BIO-BIO, INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDZ-BIOMED, ML-CS |
Module Number BIOINF4362 (entspricht BIO-4362) |
Module Title Algorithms in Bioinformatics |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Oral Presentation (about 30 minutes) and written elaboration (approx. 10 pages), leading the discussion once |
|
Content | In this seminar we look at current research topics in bioinformatics, for example, |
|
Objectives | The students can independently work with supervision on a challenging topic |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Huson | |
Literature / Other | Scientific publications |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | BIO-BIO, BIO-SEM, INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS |
Module Number BIOINF4110 (entspricht BIO-4110) |
Module Title Sequence Bioinformatics |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 9 | |
Work load - Contact time - Self study |
Work load:
270 h Class time:
90 h / 6 SWS Self study:
180 h |
|
Duration | 1 Semester | |
Frequency | In the winter semester | |
Language of instruction | English | |
Type of Exam | Written or oral exam |
|
Content | This course covers sequence-based bioinformatics and evolution. The main topics |
|
Objectives | The first aim of this course is to introduce students to advanced concepts and |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Huson | |
Literature / Other | Lecture notes and scientific publications |
|
Last offered | unknown | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | BIO-SEQ, INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-BIOINFO, MEDZ-RES, ML-CS |
Module Number INFO-4380 |
Module Title Gaze-based Human-Computer Interaction |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | In the summer semester | |
Language of instruction | English | |
Type of Exam | Written exam (if the number of participants is small, an oral exam may be required) |
|
Content | Topics: In-depth topics in visual perception (fixations, saccades, gaze patterns), mechanisms of visual attention, measurement of eye movements (eye-tracking), analysis of eye-tracking data with machine learning methods, gaze-based control of computer systems, use of gaze information for interactive systems (incl. applications in virtual (VR) and augmented reality (AR)). |
|
Objectives | The students master theoretical methods of gaze-based interaction and can apply them in a problem-oriented manner. They are able to implement specialist knowledge and research methods acquired during their studies in a project and apply them to industrial practice. The students also master machine learning methods for the analysis and interpretation of eye-tracking data in the field of human-machine interaction and are able to transfer these independently to problem areas in computer science and apply them appropriately to the situation. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Kübler | |
Literature / Other | Vorlesungsfolien, zusätzliche Literatur wird bekanntgegeben |
|
Last offered | --- | |
Planned for | --- | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, INFO-TECH, MEDI-APPL, MEDI-HCI, MEDI-INFO, MEDI-MEDI, ML-CS |
Module Number INFO-4381 |
Module Title Advanced Topics in Human-Computer Interaction |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Oral presentation of at least 30 minutes and written report (essay at least 8 pages) |
|
Content | This seminar covers current and varying topics from research and application |
|
Objectives | Students will read and reflect upon current research in the area of humancomputer |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Castner, Häufle | |
Literature / Other | - |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-HCI, MEDI-INFO, MEDI-MEDI, MEDZ-SEM, ML-CS |
Module Number INFO-4431 |
Module Title Methods of Discrete Mathematics in Computer Science |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Written Test |
|
Content | inhalt 4431 |
|
Objectives | goals 4431 |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Kaufmann | |
Literature / Other | - |
|
Last offered | - | |
Planned for | - | |
Assigned Study Areas | INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS |
Module Number INFO-4411 |
Module Title Algorithm Engineering |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Written Test |
|
Content | inhalt 4411 |
|
Objectives | goals 4411 |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Kaufmann | |
Literature / Other | - |
|
Last offered | - | |
Planned for | - | |
Assigned Study Areas | INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS |
Module Number INFO-4412 |
Module Title Algorithms and Complexity |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 9 | |
Work load - Contact time - Self study |
Work load:
270 h Class time:
90 h / 6 SWS Self study:
180 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German and English | |
Type of Exam | Written exam (in case of a small number of participants: oral tests) |
|
Content | Topics include matching, MinCostFlow, linear programming, approximation schemes, network analysis, algorithmic geometry, complexity issues such as lower bounds. |
|
Objectives | Students deepen their knowledge of algorithmic techniques in various problem areas. This includes the application of complex graph algorithms, the mastery of strategies for network analysis as well as the ability to apply and develop approximation methods themselves. In the area of complexity issues, students are able to assess problems according to their degree of difficulty and also prove these assessments using the techniques they have learned. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Kaufmann | |
Literature / Other | Raghavan, Magnati, Orlin: Network Algorithms |
|
Last offered | unknown | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS |
Module Number INFO-4415 |
Module Title Randomized Algorithms |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 9 | |
Work load - Contact time - Self study |
Work load:
270 h Class time:
90 h / 6 SWS Self study:
180 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Written Test |
|
Content | inhalt 4415 |
|
Objectives | goals 4415 |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Kaufmann | |
Literature / Other | - |
|
Last offered | - | |
Planned for | - | |
Assigned Study Areas | INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS |
Module Number INFO-4419 |
Module Title Advanced Topics in Algorithmics |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German and English | |
Type of Exam | Written exam (in case of a small number of participants: oral tests) |
|
Content | The module includes in-depth courses in algorithms that complement the basic modules in this area. It is aimed primarily at students who wish to acquire knowledge specifically in this area. |
|
Objectives | The students are able to classify special topics of algorithms and to analyse and evaluate related algorithms. They are able to transfer the concepts to new applications and design their own solution strategies. They develop their final thesis in these topics. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Kaufmann, Schlipf | |
Literature / Other | Raghavan, Magnati, Orlin: Network Algorithms |
|
Last offered | unknown | |
Planned for | Sommersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS |
Module Number INFO-4520 |
Module Title Network Algorithms |
Lecture Type(s) Practical Course |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | In the winter semester | |
Language of instruction | German and English | |
Type of Exam | Acceptance of the internship project in the course of the semester, both the presentation and the elaboration are included in the grade. |
|
Content | This practical course deepens individual practical aspects of network algorithms as addressed in corresponding courses, e.g. Methods of Algorithmics, Algorithms and Complexity, or Discrete Optimization. |
|
Objectives | The students can implement several of the methods in software technology. This implementation ranges from requirements analysis, design and implementation to text and documentation. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Kaufmann | |
Literature / Other | Originalliteratur wird bekanntgegeben |
|
Last offered | unknown | |
Planned for | Wintersemester 2023 | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, INFO-THEO, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, MEDI-WEB, ML-CS |
Module Number INFO-4653 |
Module Title Combinatorial Algorithms |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German and English | |
Type of Exam | The final grade is determined by the presentation, elaboration and participation in the discussions. |
|
Content | The seminar involves the development of written sources on topics in the areas of Efficient Algorithms under supervision. Presentation and the written summary conclude the seminar work in each case. Active participation in the individual sessions is an important part of the seminar. |
|
Objectives | The students can independently work out and understand an extended and complex subject from the area of combinatorial algorithms from a written source and present it in the form of a lecture and also represent it in a discussion in front of a plenum. In addition to the oral presentation, they can present and summarise the elaborated topic in writing. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Kaufmann | |
Literature / Other | wechselnd |
|
Last offered | unknown | |
Planned for | Sommersemester 2024 | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS |
Module Number INFO-4432 |
Module Title Discrete Optimization |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | In the summer semester | |
Language of instruction | German and English | |
Type of Exam | Written exam (oral exam with a small number of participants), exercise points |
|
Content | Topics include basics of linear optimization, methods of linear optimization, especially simplex algorithm, basics of integer optimization, branch-and-bound, cutting planes, and selected examples of of combinatorial optimization |
|
Objectives | The students know some important algorithms of linear, integer and combinatorial optimisation as well as the underlying theoretical methods. They are able to assess the methods with regard to their complexity. By formally correctly writing down the solutions and implementing the methods presented in the lecture, the students acquire necessary competences for their own scientific work. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Kaufmann | |
Literature / Other | Nemhauser, Wolsey: Integer and Combinatorial Optimization, Wiley (1999) |
|
Last offered | --- | |
Planned for | --- | |
Assigned Study Areas | INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS |
Module Number BIOINF4120 (enstpricht BIO-4120) |
Module Title Bioinformatics of Structures and Systems |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 9 | |
Work load - Contact time - Self study |
Work load:
270 h Class time:
90 h / 6 SWS Self study:
180 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Oral exam or, in case of too many students, written exam. 50% of the achievable points from the assignments and the project, individually, are required for exam admission. Points achieved in excess of 50% serve as a bonus for the final exam. |
|
Content | The lecture will focus on RNA structure and structure prediction, protein structures and their modeling, protein structure prediction, methods and concepts of systems biology, algorithms for the analysis of expression data and biological networks (concepts, inference, simulation). The lecture goes into more depth on the topics already included in the BSc module 'Fundamentals of Bioinformatics', covering in particular advanced techniques and research-related applications. Project work on research-related topics is embedded in the lecture. |
|
Objectives | Students can abstract and formalise structural and systems biology problems. They know competent applications of common procedures and tools of structural and systems bioinformatics and can apply them to biological data. They have strengthened their language competence (English) in listening comprehension, writing and presentation. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Kohlbacher | |
Literature / Other | Folien zur Vorlesung |
|
Last offered | Sommersemester 2022 | |
Planned for | Sommersemester 2024 | |
Assigned Study Areas | BIO-STRUK, INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDZ-BIOINFO, MEDZ-RES, ML-CS |
Module Number BIOINF4220 (entspricht BIO-4220) |
Module Title Integrative Bioinformatics |
Lecture Type(s) Practical Course |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | A written report is to be submitted after the course. Performance during the course will also be integrated into the final grade. |
|
Content | The basics of modelling biological data and integration of heterogeneous datasets |
|
Objectives | (1)The students learn how to parse and integrate heterogeneous biological data |
|
Prerequisite for participation | BIOINF4120 (enstpricht BIO-4120) Bioinformatics of Structures and Systems | |
Lecturer | Kohlbacher | |
Literature / Other | Will be supplied during the course. |
|
Last offered | Sommersemester 2022 | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | BIO-PRAK |
Module Number BIOINF4230 (entspricht BIO-4230) |
Module Title Applied Structure-Based Drug Design |
Lecture Type(s) Practical Course |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Written elaboration and presentation based on it |
|
Content | This module deals with the practical application of basic techniques and tools of computer-aided drug design. The program packages BALLView, VMD, Glide, Prime and Modeller are used for this purpose. Initially, the focus is on the preparation and visualization of 3D structures. In addition, specific intramolecular interactions of protein-ligand complexes are examined in more detail and selected ligands are docked into a pharmaceutically interesting target. The second part of the lab focuses on virtual high-throughput screening and rational structure-based drug design. |
|
Objectives | (1) Students are fundamentally able to handle standard tools of structure-based drug design, (2) are familiar with the practical handling of protein and ligand structures and (3) are able to interpret the results of these tools and techniques and critically assess them with regard to their relevance. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Kohlbacher | |
Literature / Other | Materialien werden zur Verfügung gestellt. |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | BIO-PRAK, MEDZ-BIOMED, MEDZ-RES |
Module Number BIOINF4352 (entspricht BIO-4352) |
Module Title Computational Proteomics and Metabolomics |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | In the winter semester | |
Language of instruction | English | |
Type of Exam | Oral exam or, in case of too many students, written exam. 50% of the achievable points from the assignments and the project, individually, are required for exam admission. Points achieved in excess of 50% serve as a bonus for the final exam. |
|
Content | Communicate the current state of the art in bioinformatics applications in proteomics and metabolomics with an emphasis on the analysis of mass spectrometry data. Topics include biological issues, experimental techniques, database searching, de novo sequencing, protein inference, quantification of peptides and metabolites, identification of metabolites. |
|
Objectives | The students know the current research standards in the field of proteomics and metabolomics and can transfer known bioinformatics techniques to problems in these fields. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Kohlbacher | |
Literature / Other | Originalarbeiten und zusätzliche Materialien werden ausgegeben. |
|
Last offered | unknown | |
Planned for | Sommersemester 2024 | |
Assigned Study Areas | BIO-BIO, INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDZ-BIOMED, MEDZ-RES, ML-CS |
Module Number BIONF4371 (bisher BIO-4371) |
Module Title Structure-Based Drug Design |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Oral exam or, in case of too many students, written exam. 50% of the achievable points from the assignments and the project, individually, are required for exam admission. Points achieved in excess of 50% serve as a bonus for the final exam. |
|
Content | Starting with a broad introduction of the pharmaceutical drug development |
|
Objectives | Students have a working knowledge on the pharmaceutical development process. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Kohlbacher | |
Literature / Other | Lecture slides and additional materials will be provided digitally. Basic knowledge of protein structure, organic chemistry, and programming skills in Python are recommended. Recommended textbooks: |
|
Last offered | Sommersemester 2022 | |
Planned for | Sommersemester 2024 | |
Assigned Study Areas | BIO-BIO, INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDZ-BIOMED, MEDZ-RES, ML-CS |
Module Number BIOINF4372 (entspricht BIO-4372) |
Module Title Cheminformatics |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Oral exam or, in case of too many students, written exam. 50% of the achievable points from the assignments and the project, individually, are required for exam admission. Points achieved in excess of 50% serve as a bonus for the final exam. |
|
Content | Starting with an overview of its main application area, namely drug design, the |
|
Objectives | Students know how different kinds of chemical data can be handled with computers, |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Kohlbacher | |
Literature / Other | Lecture slides and additional materials will be provided digitally. Basic knowledge of organic chemistry, graph theory, and programming skills in Python are recommended. Recommended textbooks: |
|
Last offered | Wintersemester 2022 | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | BIO-BIO, INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDZ-BIOMED, MEDZ-RES, ML-CS |
Module Number BIOINF4381 (entspricht BIO-4381) |
Module Title Systems Immunology |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Written elaboration and presentation based on it |
|
Content | Systems immunology links the methods of modern systems biology with applications in immunology. In this current field of research, in addition to high-throughput immunology data, mathematical modeling techniques are used to provide new insights into the dynamics of the immune system. In this module, work from the methodological foundations (systems biology) and current research on the application of these methods in immunology will be developed, thus providing an overview of this very current field of research. |
|
Objectives | Students have an overview of the field of systems immunobiology. They can apply known bioinformatics techniques to problems in immunology. They have deepened their English language and presentation skills. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Kohlbacher | |
Literature / Other | Originalarbeiten und zusätzliche Materialien werden im Seminar ausgegeben. |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | BIO-BIO, BIO-SEM, INFO-INFO, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-VIS, MEDZ-SEM, ML-CS |
Module Number BIOINF4364 (entspricht BIO-4364) |
Module Title Visualization of Biological Data |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Oral examination (written exam if there are a large number of participants) |
|
Content | As biological datasets increase in size and complexity, we are moving more |
|
Objectives | Students understand the visual analysis process. They know basic methods of |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Krone | |
Literature / Other | Lecture slides will be provided for download. Tamara Munzner ‘Visualization |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | BIO-BIO, INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-VIS, MEDZ-BIOMED, ML-CS |
Module Number INFO-4222 |
Module Title Software Quality in Theory and Industrial Practice |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | In the summer semester | |
Language of instruction | German | |
Type of Exam | Written exam (in case of a small number of participants: oral tests) |
|
Content | Most industrial systems are no longer conceivable without software. Particularly in safety-relevant areas, such as automotive or aircraft construction, software systems are increasingly being used. Classical hardware-dominated systems are gradually being replaced by software-dominated systems. The design of these software systems represents an ever increasing challenge for system developers. Growing time pressure, higher demands on correctness and increasing system complexity make software development a complex task. This inevitably increases the potential for errors. For this reason, the testing of software systems is becoming more and more important. In this lecture the basics for testing, debugging and verifying software systems are described. Main topics are among others: Quality management, function-oriented testing, coverage analysis techniques (coverage methods), input space partitioning, special testing techniques, software measurement, debugging, formal techniques, testing strategies, and testing embedded software. The lecture covers not only the theoretical basics of the listed topics, but also emphasizes the industrial practical relevance. All areas covered can be directly applied in the industrial software environment. Furthermore, the two lecturers Dr. Jürgen Ruf (Bosch Sensortec) and Prof. Dr. Thomas Kropf (Bosch), who both come from industry, bring a lot of practical experience with them and want to convey this to the students in the lecture. The lecture is based, among other things, on current research topics of the "Safety Critical Systems Group" of the Computer Engineering Department. |
|
Objectives | The students know basic principles and working techniques for ensuring high software quality and can critically question them. In addition to testing and verification, this also includes process models for software development. They are able to use analysis and test methods to increase software quality. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Bringmann, Kropf | |
Literature / Other | • Liggesmeyer, P.: Software-Qualität: Testen, Analysieren und Verifizieren |
|
Last offered | Sommersemester 2022 | |
Planned for | Sommersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, ML-CS |
Module Number INFO-4353 |
Module Title Selected Topics in Computer Security |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German | |
Type of Exam | Written exam (in case of a small number of participants: oral tests) |
|
Content | The lecture covers the following topics. Authentication and Authorization: Theory, Hardware Concepts and UNIX Concepts, Cryptography: Encryption, MACs, Signatures and Key Management, the PKCS #11 Standard and Hardware Security Modules (HSMs), Security in the Cloud, Confidential Computing, Quantum Computing. |
|
Objectives | Students have advanced knowledge of computer security with regard to hardware and software. They have practice-relevant special knowledge (basic principles according to which a computer system can be secured, security standards, cryptographic API) and can also apply this to problem solving in new and unfamiliar contexts. They are able to independently acquire new knowledge and skills and to exchange information, ideas, problems and solutions with experts at a scientific level. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Bündgen, Menth | |
Literature / Other | • C. Eckert: IT-Sicherheit, Oldenbourg Wissenschaftsverlag |
|
Last offered | unknown | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, ML-CS |
Module Number INFO-4421 |
Module Title Computability |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German | |
Type of Exam | Term Paper |
|
Content | The course gives an introduction to computability theory, covering various computability models such as partial recursive functions and Turing machines, the halting problem, and Rice's theorem. |
|
Objectives | Students acquire knowledge of the formal limits of computability |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | ||
Literature / Other | - |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS |
Module Number INFO-4422 |
Module Title Circuit Complexity |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Written Test |
|
Content | inhalt 4422 |
|
Objectives | goals 4422 |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | ||
Literature / Other | - |
|
Last offered | - | |
Planned for | - | |
Assigned Study Areas | INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS |
Module Number INFO-4441 |
Module Title Petri Nets |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Written Test |
|
Content | inhalt 4441 |
|
Objectives | goals 4441 |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | ||
Literature / Other | - |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS |
Module Number INFO-4442 |
Module Title Model Checking |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Written Test |
|
Content | inhalt 4442 |
|
Objectives | goals 4442 |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | ||
Literature / Other | - |
|
Last offered | - | |
Planned for | - | |
Assigned Study Areas | INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS |
Module Number INFO-4443 |
Module Title Formal Languages II |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Written Test |
|
Content | inhalt 4443 |
|
Objectives | goals 4443 |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | ||
Literature / Other | - |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS |
Module Number INFO-4444 |
Module Title Complexity Theory II |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German | |
Type of Exam | Oral examination (written exam if there are a large number of participants) |
|
Content | Building on the lecture Complexity Theory, the in-depth topics include (non) uniform circuit classes, approximation theory, and randomization. In addition, barriers in the form of relativization and natural proofs are considered. |
|
Objectives | Students have an overview of different complexity classes, circuits and randomisation and are able to write a master thesis in this field. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | ||
Literature / Other | - |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS |
Module Number INFO-4497 |
Module Title Special Chapters in Theoretical Computer Science I |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Written Test |
|
Content | inhalt 4497 |
|
Objectives | goals 4497 |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | ||
Literature / Other | - |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS |
Module Number INFO-4498 |
Module Title Special Chapters in Theoretical Computer Science II |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Written Test |
|
Content | inhalt 4498 |
|
Objectives | goals 4498 |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | ||
Literature / Other | - |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS |
Module Number INFO-4499 |
Module Title Special Chapters in Theoretical Computer Science III |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 9 | |
Work load - Contact time - Self study |
Work load:
270 h Class time:
90 h / 6 SWS Self study:
180 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Written Test |
|
Content | inhalt 4499 |
|
Objectives | goals 4499 |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | ||
Literature / Other | - |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS |
Module Number INFO-4656 |
Module Title Seminar Theoretical Computer Science |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | course-dependent |
|
Content | Varying topics in the field of theoretical computer science; depending on the specific seminar topic (see seminar offers in alma). Note on the summer term 2024: |
|
Objectives | In-depth knowledge of approaches and methods in theoretical computer science |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Schlipf, wechselnde Dozenten | |
Literature / Other | je nach Seminarthema |
|
Last offered | Wintersemester 2022 | |
Planned for | Sommersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS |
Module Number INFO-4167 |
Module Title Computer Graphics |
Lecture Type(s) Practical Course |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | In the summer semester | |
Language of instruction | English | |
Type of Exam | Project, presentation and elaboration |
|
Content | Implementation of advanced applications and programs in the field of computer graphics / computer vision |
|
Objectives | Students know how current approaches in the areas of rendering, GPU-based programming, displays or computational photography can be efficiently implemented with appropriate hardware. They can independently plan and implement programming projects in groups using programming languages developed for GPUs, input and output hardware and suitable libraries. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Lensch | |
Literature / Other | Entwicklungsumgebung wird zur Verfügung gestellt |
|
Last offered | unknown | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-PRAX, MEDI-VIS, ML-CS |
Module Number INFO-4173 |
Module Title Massively Parallel Computing |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | In the summer semester | |
Language of instruction | English | |
Type of Exam | Oral examination (written exam if there are a large number of participants), successful exercises can result in a grade bonus |
|
Content | The lecture introduces the necessary concepts of parallel processing, and gives an overview of the currently available hardware. Furthermore, basic parallel algorithms, e.g. Map, Reduce, Prefix Sum, Branching, but also parallel applications like FFT, particle systems and simulations etc. are covered. In order to develop efficient parallel solutions for new problems, appropriate approaches and complexity analyses will be taught. |
|
Objectives | A current trend of all chip manufacturers is to integrate more and more computing units on one chip, e.g. with several hundred processors on one graphics card. In order to use these architectures efficiently, suitable algorithms must be chosen and the problems optimised in terms of memory bandwidth. (1) The aim of the lecture is to enable the students to analyse a given problem with regard to the possible increase in efficiency through parallelisation. (2) They are able to develop suitable algorithms to work out a massively parallel implementation as fast as possible. (3) They are able to optimise their programs in terms of memory bandwidth, GPU utilisation and registers by profiling. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Lensch | |
Literature / Other | Hubert Nguyen: GPU Gems 3, Addison Wesley; T. Mattson, B. Sanders, B. |
|
Last offered | Sommersemester 2022 | |
Planned for | Sommersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, MEDI-PRAX, MEDI-VIS, MEDZ-MEDTECH, ML-CS |
Module Number INFO-4174 |
Module Title Massively Parallel Computing |
Lecture Type(s) Practical Course |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | In the summer semester | |
Language of instruction | English | |
Type of Exam | Presentation and written report |
|
Content | The efficient implementation and realization of algorithms from different areas of computer science or related disciplines on massively parallel architectures will be taught. Furthermore, the programming of massively parallel computer systems GPU, and the associated challenges such as memory management, branching, synchronization are covered. In addition to GPU programming, the focus is also on measuring and comparing the performance of parallel applications. |
|
Objectives | Students can independently (in small groups) plan, implement and execute the implementation of computationally intensive tasks on massively parallel computers. They are able to measure and analyse the runtime of parallel applications. |
|
Prerequisite for participation | INFO-4173 Massively Parallel Computing | |
Lecturer | Lensch | |
Literature / Other | Entwicklungsumgebung wird zur Verfügung gestellt, NVIDIA CUDA page |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, MEDI-PRAX, MEDI-VIS, MEDZ-MEDTECH, ML-CS |
Module Number INFO-4175 |
Module Title Rendering |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | In the winter semester | |
Language of instruction | English | |
Type of Exam | Oral examination (written exam if there are a large number of participants), successful exercises can result in a grade bonus |
|
Content | In-depth instruction in computer graphics with a focus on image synthesis is taught and practiced. The module covers basic and advanced theory and algorithms of Monte Carlo and quasi-MonteCarlo simulation, covers global illumination approaches such as path tracing, bidirectional path tracing, Metropolis sampling, photon mapping, as well as methods for real-time rendering. |
|
Objectives | Students acquire in-depth knowledge in the areas of Monte Carlo approximation and global illumination simulation. Students will be able to analyse and implement the techniques covered as well as independently evaluate and solve problems and apply them in their own projects. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Lensch | |
Literature / Other | Pharr, Humphreys: Physically Based Rendering, Morgan Kaufmann, 2004 Dutré et al.: Advanced G / Grundkenntnisse im Bereich Computergrafik |
|
Last offered | unknown | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-HCI, MEDI-INFO, MEDI-MEDI, MEDI-VIS, ML-CS |
Module Number INFO-4176 |
Module Title Computational Photography |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | In the summer semester | |
Language of instruction | English | |
Type of Exam | Oral examination (written exam if there are a large number of participants), successful exercises can result in a grade bonus |
|
Content | In-depth instruction in digital photography, hardware, and subsequent image reconstruction and processing will be taught and practiced. The course covers basic and advanced theory and algorithms of image reconstruction, denoising, deconvolution, 3D image acquisition, computed tomography or compressive sensing. At the same time, different acquisition systems, camera sensors, active illumination and multi-camera systems will be covered, as well as the new image acquisition modalities that they enable. |
|
Objectives | Students acquire in-depth knowledge in the areas of digital photography, computational imaging, and image-based processes. Students will be able to analyse the techniques covered and compare alternative approaches. In projects, you will be able to independently evaluate the problem and develop proposals for solutions. Students are able to realise solutions through implementations in software with the appropriate hardware. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Lensch | |
Literature / Other | Vorlesungsfolien werden bereitgestellt / Grundkenntnisse im Bereich Computergrafik |
|
Last offered | Sommersemester 2022 | |
Planned for | Sommersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-VIS, MEDZ-MEDTECH, ML-CS |
Module Number INFO-4178 |
Module Title Displays |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | In the winter semester | |
Language of instruction | English | |
Type of Exam | Oral examination (written exam if there are a large number of participants), successful exercises can result in a grade bonus |
|
Content | In-depth teaching content in the area of displays is taught and practiced. The module covers the structure and technology of monitors, projectors, AR/VR glasses, stereo displays, light field displays and other frahling methods. At the same time, the focus is on the algorithmic preparation of data for all types of displays. |
|
Objectives | Students acquire in-depth knowledge of current display technologies. The students will be able to analyse the technologies discussed and evaluate alternatives. They can independently evaluate problems and develop their own solutions and implementations in projects. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Lensch | |
Literature / Other | Aktuelle Veröffentlichungen im Bereich Displays, Vorlesungsfolien werden bereitgestellt / Grundkenntnisse im Bereich Computergrafik |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, INFO-TECH, MEDI-APPL, MEDI-HCI, MEDI-INFO, MEDI-MEDI, MEDI-MMT, MEDI-VIS, ML-CS |
Module Number INFO-4341 |
Module Title Network Security I |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German and English | |
Type of Exam | Written exam (oral exam if the number of participants is small), exercises can be Bonus points will be considered into the exam. |
|
Content | The lecture covers the following topics: Principles of Network Security, Cryptographic Methods, Public Key Infrastructure, Authentication, Application Layer Security, Transport Layer Security, Virtual Private Networks, Layer-2 Security, Perimeter Security, Anonymization, Blockchain, Advanced Topics; the lecture is accompanied by an extensive tutorial, which illustrates and deepens the acquired knowledge with practical examples. |
|
Objectives | Students have a comprehensive and in-depth understanding of network security. They are able to apply their acquired problem-solving skills in new and unfamiliar contexts. They are able to acquire new knowledge and skills independently and to exchange information, ideas, problems and solutions with experts on a scientific level. |
|
Prerequisite for participation | INF3331 Computer Networking and Internet | |
Lecturer | Menth | |
Literature / Other | - |
|
Last offered | Wintersemester 2021 | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-WEB, ML-CS |
Module Number INFO-4342 |
Module Title Network Security II (3 ECTS) |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German and English | |
Type of Exam | Written exam (oral exam if the number of participants is small), points gained in the exercises may be transferable as bonus points into the exam. |
|
Content | The lecture covers the following topics: Layer-2 Security, Perimeter Security, Anonymization, Blockchain, Advanced Topics; the lecture is accompanied by an extensive practice session that illustrates and deepens the acquired knowledge with practical examples. |
|
Objectives | Network Security II: Students have a comprehensive and in-depth understanding of network security. They are able to apply their acquired problem-solving skills also in new and unfamiliar contexts. They are able to acquire new knowledge and skills independently and to exchange information, ideas, problems and solutions with experts on a scientific level. |
|
Prerequisite for participation | INFO-4341 Network Security I | |
Lecturer | Menth | |
Literature / Other | - |
|
Last offered | Wintersemester 2021 | |
Planned for | Sommersemester 2023 | |
Assigned Study Areas | INFO-INFO, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-WEB, ML-CS |
Module Number INFO-4343 |
Module Title Network Security III (Lab) |
Lecture Type(s) Practical Course |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German and English | |
Type of Exam | Graded internship attempts consisting of theory and practice with a final exam or oral exam |
|
Content | Introductory lecture sessions for each experiment, hands-on exercises at home to become familiar with the experimental environment (Linux command line, basic network administration commands, traffic recording), and graded classroom exercises in the experimental lab on the following topics: Network security, attacks and attack defense, VPN, advanced routing methods, wifi, selected application protocols. |
|
Objectives | The students deepen their practical knowledge of communication networks considerably, especially with regard to practical, security-relevant aspects. They can independently acquire new knowledge and skills and apply their problem-solving abilities in new and unfamiliar contexts. They learn to communicate their knowledge in a clear and unambiguous manner and to exchange it at a scientific level. |
|
Prerequisite for participation |
INF3331 Computer Networking and Internet, INFO-4341 Network Security I, INFO-4342 Network Security II (3 ECTS) |
|
Lecturer | Menth | |
Literature / Other | - |
|
Last offered | Wintersemester 2021 | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-TECH, MEDI-APPL, MEDI-INFO, ML-CS |
Module Number INFO-4344 |
Module Title Communication Networks Lab |
Lecture Type(s) Practical Course |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German and English | |
Type of Exam | Graded internship attempts and an oral or written final exam |
|
Content | Introductory lecture sessions for each experiment, hands-on exercises at home to become familiar with the experimental environment (Linux command line, basic network administration commands, traffic recording), and graded classroom exercises in the experimental lab on advanced topics. Alternate Topics on current communication technologies. |
|
Objectives | Students deepen their practical knowledge of communication networks significantly, especially on the topics taught. They can independently carry out very demanding configurations of computer networks and experimentally evaluate properties of advanced protocols. They can independently acquire new knowledge and skills and apply their problem-solving abilities in new and unfamiliar contexts. They learn to communicate and exchange their knowledge in a clear and unambiguous manner at a scientific level. |
|
Prerequisite for participation | INF3331 Computer Networking and Internet | |
Lecturer | Menth | |
Literature / Other | - |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | INFO-INFO, INFO-TECH, MEDI-APPL, MEDI-INFO, ML-CS |
Module Number INFO-4345 |
Module Title Modeling and Simulation I |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German and English | |
Type of Exam | Exam, exercise performance can flow into the exam as bonus points |
|
Content | Statistical basics (part 1), introduction to simulation techniques, distribution functions (part 1), sample preparation, statistical basics (part 2), statistical evaluation of simulations, stochastic processes, discrete-time Markov chains, continuous-time Markov chains; the lecture contents are put into practice during the exercise, in particular a simple simulator is built in several consecutive exercises. The focus is on the modeling of problems from different contexts by means of suitable distribution functions as well as by Markov chains, and on the associated mathematical foundations. |
|
Objectives | The students can model and examine technical systems in their conception phase with sophisticated methods and thus efficiently participate in research and development. They have in-depth, practical knowledge of discrete-time simulation and can systematically set up and evaluate experiments. They can use discrete-time and continuous-time Markov chains for the modelling and investigation of technical systems and predict their performance with the help of queueing theory. They are able to transfer and apply the acquired knowledge in new contexts. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Menth | |
Literature / Other | - |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS |
Module Number INFO-4346 |
Module Title Modeling and Simulation II |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German and English | |
Type of Exam | Exam, exercise performance can flow into the exam as bonus points |
|
Content | Distribution functions (part 2), multidimensional random structures, design of experiments, significance tests of hypotheses and their applications, investigation of measured data, time-dependent statistics, possibility and limits of model building and simulation, random number generation; the lecture conveys the theoretical basics and in the exercise the lecture contents are implemented by programming tasks based on practical examples. |
|
Objectives | Students will be able to model and examine technical systems in their conception phase using sophisticated methods and thus efficiently contribute to research and development. They are able to transfer and apply the acquired knowledge in new contexts. They can critically question learned methods and thus decide whether they are suitable for given problems. Through the acquired mathematical understanding, they can modify learned tools for new problems in a suitable manner. |
|
Prerequisite for participation | INFO-4345 Modeling and Simulation I | |
Lecturer | Menth | |
Literature / Other | - |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS |
Module Number INFO-4347 |
Module Title Network Softwarization |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German | |
Type of Exam | Written exam (oral exam if the number of participants is small), points gained in the exercises may be transferable as bonus points into the exam. |
|
Content | The lecture gives an overview of relevant technologies in the area of Network Softwarization. This includes Software Defined Networking using Open Flow, Data Plane Programming using P4 as well as Virtualization techniques and Network Function Virtualization. Additionally, current research topics will be discussed. The course is accompanied by practical exercises in which the students apply the learned technologies and solve challenging programming tasks. |
|
Objectives | Students have acquired state-of-the-art knowledge and skills in network softwarization and insight into research topics, which makes them particularly qualified for research work in the field of communication networks. They have an in-depth understanding of how network software can be used to develop new technologies faster and operate networks more efficiently. They can acquire new knowledge and skills independently and carry out application-oriented projects in a largely self-directed manner. They are able to exchange information, ideas, problems and solutions with experts and laypersons on a scientific level. |
|
Prerequisite for participation |
INF3331 Computer Networking and Internet, INFO-4341 Network Security I, INFO-4342 Network Security II (3 ECTS) |
|
Lecturer | Menth | |
Literature / Other | - |
|
Last offered | Sommersemester 2022 | |
Planned for | Sommersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-WEB, ML-CS |
Module Number INFO-4348 |
Module Title Communication Technologies 1 |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German | |
Type of Exam | Written exam (oral exam if the number of participants is small), points achieved in the exercises may be considered as bonus points in the exam. |
|
Content | The lecture provides knowledge on advanced topics in the field of communication networks, in contrast to INF3331 "Grundlagen des Internets" this lecture deals mainly with the communication layers below the IPLayer. Topics are: Data and signals, conversion of data into digital signals, modulation of digital and analog signals, multiplex techniques, transmission media, switching, data link control, multiple access protocols, Ethernet, backbone concepts, VLANs, software-defined networking, quality of service, time-sensitive networking (TSN), bus systems in vehicles. |
|
Objectives | Communication Technologies 1: Students have a comprehensive and deep understanding of the operating principle and organisation of communication networks. They are able to exchange information, ideas, problems and solutions with experts and non-experts at a scientific level. They can independently acquire new knowledge and skills. They have acquired a critical understanding at the cutting edge of knowledge in several specialised areas and can apply their problem-solving skills in new and unfamiliar situations. |
|
Prerequisite for participation | INF3331 Computer Networking and Internet | |
Lecturer | Menth | |
Literature / Other | - |
|
Last offered | unknown | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-TECH, MEDI-APPL, MEDI-INFO, ML-CS |
Module Number INFO-4349 |
Module Title Communication Technologies 2 |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German | |
Type of Exam | Written exam (oral exam if the number of participants is small), exercises can be considered as bonuspoints into the exam. |
|
Content | The lecture provides knowledge on advanced topics in the field of communication networks, in contrast to INF3331 "Grundlagen des Internets" this lecture deals mainly with the communication layers below the IPLayer. |
|
Objectives | Students have a comprehensive and deep understanding of the operating principle and organisation of communication networks. They are able to exchange information, ideas, problems and solutions with experts and non-experts at a scientific level. They are able to acquire new knowledge and skills independently. They have acquired a critical understanding at the cutting edge of knowledge in several specialised areas and can apply their problem-solving skills in new and unfamiliar situations. |
|
Prerequisite for participation | INFO-4348 Communication Technologies 1 | |
Lecturer | Menth | |
Literature / Other | - |
|
Last offered | unknown | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-WEB, ML-CS |
Module Number INFO-4350 |
Module Title Selected Topics in Communication Networks |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German | |
Type of Exam | Written exam (in case of a small number of participants: oral tests) |
|
Content | Changing in-depth topics from the subfields of the research field of communication networks. |
|
Objectives | The students have in-depth knowledge in the field of communication networks. They can apply their acquired problem-solving skills in new and unfamiliar contexts. They are able to independently acquire new knowledge and skills in the subject area and carry out independent research or application-oriented projects. They are able to exchange information, ideas, problems and solutions with experts and non-experts at a scientific level. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Menth | |
Literature / Other | - |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | INFO-INFO, INFO-TECH, MEDI-APPL, MEDI-INFO, ML-CS |
Module Number INFO-4354 |
Module Title Public Cloud Computing |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German | |
Type of Exam | Written exam (oral exam if the number of participants is small), points received in exercises may be transferable as bonus points into the exam. |
|
Content | The lecture provides advanced knowledge in container technology, Software-as-a-Service (SaaS) and Function-as-a-Service (FaaS). Theoretical approaches to cloud architectures, cloud computing patterns and automation are discussed. Classic use cases such as serverless computing, Internet of Things (IoT), edge computing, business intelligence (BI) and machine learning (ML) will be demonstrated. The tutorial consists of extensive, hands-on assignments and specifically addresses operations, economic efficiency, and security. |
|
Objectives | The students have advanced, current knowledge in the area of public cloud computing. They can independently acquire new knowledge and skills and carry out largely self-directed application-oriented projects. They are able to exchange information, ideas, problems and solutions with experts and laypersons on a scientific level and to assume responsibility in a team. |
|
Prerequisite for participation |
INF3331 Computer Networking and Internet, INFO-4341 Network Security I |
|
Lecturer | Menth | |
Literature / Other | - |
|
Last offered | unknown | |
Planned for | Wintersemester 2023 | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, INFO-TECH, MEDI-APPL, MEDI-INFO, ML-CS |
Module Number INFO-4352 |
Module Title Pentesting |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | In the winter semester | |
Language of instruction | German | |
Type of Exam | Written exam (in case of a small number of participants: oral tests) |
|
Content | To secure networks or applications, an administrator or developer needs knowledge about existing vulnerabilities. These can be efficiently uncovered through simulated hacker attacks, so-called penetration tests. In addition to theoretical basics about the planning and execution of penetration tests, this lecture provides in-depth practical knowledge of modern attack tools, current vulnerabilities and the methodology to exploit them. The spectrum ranges from footprinting to the actual attack to the placement of backdoors in a compromised system. Lecture and exercises will take place as a closely interlinked block course. Topics are: Penetration testing design options, testing modules, estimating testing effort, assessing results and documentation, tracking vulnerabilities, ethical and legal issues, penetration testing standards, footprinting, portscanning, enumeration, sniffing, attacks against encryption, common configurational vulnerabilities, methodical security analysis of web applications and typical web application vulnerabilities, dealing with metasploit, attacks against Windows networks, privilege escalation, backdoors, online and offline attacks against passwords, vulnerability analysis, exploitation of buffer overflow vulnerabilities. |
|
Objectives | Students significantly deepen their understanding of IT systems and are enabled to recognise and remedy security vulnerabilities. They have the ability to apply the knowledge in new, unfamiliar contexts and to acquire new knowledge independently. In addition, they learn to adequately include ethical aspects. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Menth | |
Literature / Other | - |
|
Last offered | unknown | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, ML-CS |
Module Number INFO-4351 |
Module Title Communication Networks (Seminar) |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Oral presentation and written report |
|
Content | Dieses Seminar behandelt aktuelle und wechselnde Themen aus Forschung und Anwendung auf dem Gebiet der Kommunikationsnetze. |
|
Objectives | Students are able to read, reflect, and examine the topic in substance upon |
|
Prerequisite for participation | INFO-4348 Communication Technologies 1 | |
Lecturer | Menth | |
Literature / Other | - |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-WEB, MEDZ-SEM, ML-CS |
Module Number BIOINF4376 (entspricht BIO-4376) |
Module Title Biomedical Data Management |
Lecture Type(s) Lecture, Seminar |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | In the winter semester | |
Language of instruction | English | |
Type of Exam | Oral exam or written exam with a large number of participants, successful participation in the seminar is a prerequisite for the exam |
|
Content | Topics include the various technologies of quantitative biology/biomedicine (e.g. omics technologies, high-throughput screening and imaging), methods for processing and analyzing high-throughput data (bioinformatics workflows) and standardization of data exchange formats. Furthermore, the module deals with data and metadata models, as well as data storage (different approaches) and general concepts for data management (e.g. different database systems) and (web-based) visualization. The lecture thereby prepares the technical basics of the topics and in the seminar current work on biomedical application of these technologies is presented and discussed by the students. |
|
Objectives | Students will master simple and multivariate statistical methods, as well as data-driven approaches for the management and analysis of high-throughput biomedical and imaging data. They are able to design research infrastructures and to use evaluation routines on different already existing infrastructures in a methodologically adequate way, as well as to critically question their use in publications. Furthermore, the students are able to design, conduct and adequately evaluate a qualitative/quantitative investigation (e.g. a clinical study or a large-scale research project) (e.g. in research-oriented courses of the degree programme or in the Master's thesis). In addition, they can classify the dangers and opportunities of "open data" and discuss them in an interdisciplinary manner. The students can also assess the strengths and weaknesses of qualitative and quantitative data-driven research and critically evaluate the methodological quality of the publications. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Nahnsen | |
Literature / Other | - |
|
Last offered | Sommersemester 2022 | |
Planned for | Sommersemester 2024 | |
Assigned Study Areas | BIO-BIO, INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-BIOMED, MEDZ-SEM, ML-CS |
Module Number BIOINF4210 (entspricht BIO-4210) |
Module Title Practical Transcriptomics |
Lecture Type(s) Practical Course |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | The final grade is based on performance, a written report on each day of the practical course, and one or two short oral presentations. |
|
Content | The focus is on the practical analysis of so-called next generation sequencing |
|
Objectives | Students will gain practical experience in designing and programming bioinformatics |
|
Prerequisite for participation |
BIOINF3330 Expressions Bioinformatics, BIOINF4110 (entspricht BIO-4110) Sequence Bioinformatics, BIOINF4120 (enstpricht BIO-4120) Bioinformatics of Structures and Systems |
|
Lecturer | Nieselt | |
Literature / Other | Will be provided at the beginning of the course, if necessary. |
|
Last offered | unknown | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | BIO-PRAK, MEDZ-BIOMED |
Module Number BIOINF4331 (entspricht BIO-4331) |
Module Title Advances in Computational Transcriptomics |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | In the winter semester | |
Language of instruction | English | |
Type of Exam | Written exam (in case of a small number of participants: oral tests) |
|
Content | Functional genomics, i.e. the interpretation of a genome to determine the biological |
|
Objectives | The students are familiar with the new bioinformatics findings on expression |
|
Prerequisite for participation | BIOINF3330 Expressions Bioinformatics | |
Lecturer | Nieselt | |
Literature / Other | Own lecture notes and selected articles |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | BIO-BIO, INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-BIOMED, ML-CS |
Module Number BIOINF4363 (entspricht BIO-4363) |
Module Title RNA Bioinformatics |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Oral Presentation (about 30 minutes) and written elaboration (approx. 10 pages), leading the discussion once |
|
Content | In this seminar, current topics related to computer-aided RNA bioinformatics |
|
Objectives | The students can independently work with supervision on a challenging topic |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Nieselt | |
Literature / Other | Articles / scientific publications for each individual topic |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | BIO-BIO, BIO-SEM, INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-BIOMED, MEDZ-SEM, ML-CS |
Module Number BIOINF4373 (entspricht BIO-4373) |
Module Title Bioinformatics and Machine Learning |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Oral Presentation (about 30 minutes) and written elaboration (approx. 10 pages), leading the discussion once |
|
Content | In this seminar, machine learning approaches with applications to bioinformatics |
|
Objectives | The students can independently work with supervision on a challenging topic |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Nieselt | |
Literature / Other | Articles / scientific publications for each individual topic |
|
Last offered | unknown | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | BIO-BIO, BIO-SEM, INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-BIOMED, MEDZ-SEM, ML-CS |
Module Number BIOINF4365 (entspricht BIO-4365) |
Module Title Introduction to next-generation sequencing |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Written Test |
|
Content | inhalt 4365 |
|
Objectives | goals 4365 |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Ossowski | |
Literature / Other | - |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | BIO-BIO, INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-BIOMED, ML-CS |
Module Number INFO-4241 |
Module Title Programming Languages II |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Written or oral examination. Participation in exercises is required for exam participation. |
|
Content | This lecture is about the semantics and type systems of modern programming |
|
Objectives | Students will be able to discuss and analyze modern programming languages in |
|
Prerequisite for participation | INF3181 Programming Languages I | |
Lecturer | Brachthäuser, Ostermann | |
Literature / Other | Benjamin C. Pierce. Types and Programming Languages. MIT Press, 2003. |
|
Last offered | Wintersemester 2021 | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS |
Module Number INFO-4242 |
Module Title Programming Languages III |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Written Test |
|
Content | info 4242 |
|
Objectives | goals 4242 |
|
Prerequisite for participation |
INF3181 Programming Languages I, INFO-4241 Programming Languages II |
|
Lecturer | Ostermann | |
Literature / Other | - |
|
Last offered | - | |
Planned for | - | |
Assigned Study Areas | INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS |
Module Number INFO-4481 |
Module Title Topics in Programming Language Theory |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German and English | |
Type of Exam | To be announced. |
|
Content | The seminar includes the elaboration of written sources on topics related to the theory of programming languages. Presentation and the written summary conclude the seminar work. Active participation in each session is an important part of the seminar. The title of the seminar can vary depending on the semester and the concrete seminar topics. Please refer to the course catalogue in alma for the seminar offer in a specific semester. |
|
Objectives | The students can independently develop and understand an extended and complex subject from the field of programming language theory from a written source and present it in the form of a lecture and also represent it in a discussion in front of a plenum. In addition to the oral presentation, they can present and summarise the elaborated topic in writing. |
|
Prerequisite for participation |
INF3181 Programming Languages I, INFO-4241 Programming Languages II |
|
Lecturer | Ostermann | |
Literature / Other | wird bekannt gegeben |
|
Last offered | unknown | |
Planned for | Sommersemester 2024 | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS |
Module Number INFO-4244 |
Module Title Programming Languages and Techniques |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German and English | |
Type of Exam | Will be announced in the respective seminar, |
|
Content | The seminar includes the development of written sources on topics related to programming and programming languages. Presentation and the written summary conclude the seminar work. Active participation in the individual sessions is an important part of the seminar. For the respective seminar offers in a given semester please refer to the course catalogue in alma. |
|
Objectives | The students can independently develop and understand an extended and complex subject from the field of programming techniques from a written source and present it in the form of a lecture and also represent it in a discussion in front of a plenum. In addition to the oral presentation, they can present and summarise the elaborated topic in writing. |
|
Prerequisite for participation |
INF3181 Programming Languages I, INFO-4241 Programming Languages II, INFO-4242 Programming Languages III |
|
Lecturer | Ostermann, Plümicke | |
Literature / Other | wird bekannt gegeben |
|
Last offered | Wintersemester 2022 | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS |
Module Number INFO-4245 |
Module Title Software Project Management |
Lecture Type(s) Practical Course |
---|---|---|
ECTS | 9 | |
Work load - Contact time - Self study |
Work load:
270 h Class time:
90 h / 6 SWS Self study:
180 h |
|
Duration | 1 Semester | |
Frequency | In the winter semester | |
Language of instruction | German | |
Type of Exam | Graded reports and presentations as well as success in team leadership are included in the grade. |
|
Content | In this internship, you will lead a group of students in the implementation of a software project as part of the "Tübingen Software Project". This includes the management of the project as well as the technical leadership, which includes aspects such as workflow configuration, social coding, quality management, continuous integration and testing. Through specialized training, including from our industry partners,in the aspects listed above, we will prepare you for this role. This internship will last for a full year. |
|
Objectives | Participants are able to lead a small group of software developers and take over the technical and organisational management of a medium-sized software project. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Ostermann | |
Literature / Other | Alle Materialien werden bereitgestellt. / Erfahrungen mit einem größeren Softwareprojekt sind sehr hilfreich. |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS |
Module Number INFO-4247 |
Module Title Algorithmic Action |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 9 | |
Work load - Contact time - Self study |
Work load:
270 h Class time:
90 h / 6 SWS Self study:
180 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Written Test |
|
Content | inhalt 4247 |
|
Objectives | goals 4247 |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Ostermann | |
Literature / Other | - |
|
Last offered | - | |
Planned for | - | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, ML-CS |
Module Number INFO-4248 |
Module Title Interactive Theorem Proving |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 9 | |
Work load - Contact time - Self study |
Work load:
270 h Class time:
90 h / 6 SWS Self study:
180 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German and English | |
Type of Exam | Written or oral examination. Participation in exercises is required for exam participation. |
|
Content | This course is an introduction to interactive theorem programming and advanced functional programming, mostly using the Coq proof assistant. |
|
Objectives | Students will be able to write programs and prove theorems in the Coq proof assistant. Students understand the theoretical underpinnings of interactive theorem |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Ostermann | |
Literature / Other | Volume 1 and 2 of the “Software Foundations” series available at https://softwarefoundations.cis.upenn.edu/. |
|
Last offered | unknown | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS |
Module Number INFO-4249 |
Module Title Advanced Topics in Programming Languages |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German and English | |
Type of Exam | Written exam (oral exam if the number of participants is small), points gained in the exercises may be transferable as bonus points into the exam. |
|
Content | Changing in-depth topics from the subfields of the research field of programming languages. |
|
Objectives | Students have knowledge of research methodology in the field of programming languages. They are prepared for writing scientific papers, especially in sub-areas of this research field. The students can prepare specifically for Master's theses and doctoral projects. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Ostermann | |
Literature / Other | wird bekannt gegeben |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, ML-CS |
Module Number INFO-4243 |
Module Title Application of programming language technologies |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Written Test |
|
Content | info 4243 |
|
Objectives | goals 4243 |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Ostermann | |
Literature / Other | - |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS |
Module Number MEDZ-4110 |
Module Title Advanced Medical Informatics |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 9 | |
Work load - Contact time - Self study |
Work load:
270 h Class time:
90 h / 6 SWS Self study:
180 h |
|
Duration | 1 Semester | |
Frequency | In the winter semester | |
Language of instruction | English | |
Type of Exam | Written exam or oral exam |
|
Content | This lecture comprises different areas of Medical Informatics. The focus is on |
|
Objectives | The students are capable of explaining the most important terms, methods and |
|
Prerequisite for participation | MDZINFM1410 Introduction to Medical Informatics | |
Lecturer | Pfeifer | |
Literature / Other | Eta S. Berner: Clinical Decision Support Systems - Theory and Practice, |
|
Last offered | Wintersemester 2022 | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | BIO-BIO, INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-BIOMED, ML-CS |
Module Number MEDZ-4991 |
Module Title Medical Data Science |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | In the winter semester | |
Language of instruction | English | |
Type of Exam | Written Test |
|
Content | This lecture comprises different areas of Medical Data Science. Data Science or statistical machine learning methods have the potential to transform personal health care over the coming years. Advances in the technologies have generated large biological data sets. In order to gain insights that can then be used to improve preventive care or treatment of patients, these big data have to be stored in a way that enables fast querying of relevant characteristics of the data and consequently building statistical models that represent the dependencies between variables. These models can then be utilized to derive new biomedical principals, provide evidence for or against certain hypotheses, and to assist medical professionals in their decision process. Specific topics are: Method-wise, the lecture introduces methods for GWAS analyses (e.g., LMMs), methods for sequence analysis (e.g., kernel methods), methods for “small n problems” (e.g., domain adaptation, transfer learning, and multitask learning), |
|
Objectives | The students are capable of explaining the most important terms, methods and theories in the data science area with focus on the analysis of biomedical data. They are enabled to decide which type of methods fit to which kind of data sets. The students can critically reflect on shortcomings of state-of-the-art methods |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Pfeifer | |
Literature / Other | Trevor Hastie, Robert Tibshirani, Jerome Friedman: The Elements of Statistical |
|
Last offered | Wintersemester 2022 | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | BIO-BIO, INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-BIOMED, MEDZ-MEDTECH, ML-CS, ML-DIV |
Module Number MEDZ-4520 |
Module Title Biomedical Informatics Methods for Infection Research |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Oral presentation and written report |
|
Content | This seminar covers different aspects of biomedical informatics methods for |
|
Objectives | Successful students know the most important terms, theories and methods in |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Pfeifer | |
Literature / Other | The papers will be announced at the first meeting. / recommended: Machine learning: theory and algorithms or Introduction to Statistical |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | BIO-BIO, BIO-SEM, INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS |
Module Number MEDZ-4521 |
Module Title Computer Science Methods for Privacy Preservation in Biomedical Studies |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Talk and Report |
|
Content | This seminar covers different aspects of computer science methods for privacy preservation of medical data as well as for personalized medicine. This includes computer science methods to support research in the following areas: |
|
Objectives | Successful students know the most important terms, theories and methods in the field of privacy preservation with computer science methods and know how to critically reflect on them. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Pfeifer | |
Literature / Other | - |
|
Last offered | unknown | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | BIO-BIO, BIO-SEM, INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS |
Module Number MEDZ-4522 |
Module Title Machine Learning for Health |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | In the winter semester | |
Language of instruction | English | |
Type of Exam | Oral presentation and written report |
|
Content | This seminar covers different state-of-the-art machine learning methods on biomedical |
|
Objectives | Machine Learning for Health: Successful students know the most important terms, theories and methods in |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Pfeifer | |
Literature / Other | The papers will be announced at the first meeting. / recommended: Machine learning: theory and algorithms or Introduction to Statistical |
|
Last offered | Wintersemester 2022 | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS, ML-DIV |
Module Number MEDZ-4250 |
Module Title Machine Learning in Biomedicine |
Lecture Type(s) Practical Course |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Written Test |
|
Content | inhalt medz 4251 |
|
Objectives | goals medz 4250 |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Pfeifer | |
Literature / Other | - |
|
Last offered | unknown | |
Planned for | Sommersemester 2024 | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, MEDZ-BIOMED, MEDZ-MEDTECH, ML-CS, ML-DIV |
Module Number ML-4530 |
Module Title Deep Learning for Vision and Graphics |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Oral Test |
|
Content | The fields of 3D computer vision and graphics have been revolutionized by deep learning. For example, it is now possible to obtain detailed 3D reconstructions of humans and objects from single images, generate photo-realistic renderings of 3D scenes with neural networks, or manipulate and edit videos and images. In this seminar, we will cover the most recent publications and advances in the fields of neural rendering, 3D computer vision, 3D shape reconstruction, and representation learning for 3D shapes. |
|
Objectives | Students are able to read and reflect upon current research papers in this research area. They can critically assess the contributions of such a paper. They can present current research results to other students and researchers and can lead research discussions. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Pons-Moll | |
Literature / Other | Will be announced in the first meeting / Programming skills, knowledge of linear algebra and calculus, numerical optimization, probability theory. |
|
Last offered | Sommersemester 2022 | |
Planned for | Sommersemester 2024 | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS, ML-DIV |
Module Number INFO-4314 |
Module Title Programming of Mobile Embedded Systems |
Lecture Type(s) Practical Course |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | In the winter semester | |
Language of instruction | German | |
Type of Exam | Final Project Presentation and Report |
|
Content | This module provides hands-on experience in designing and programming mobile embedded systems (ES). Participants will work in teams of up to three students and in three groups to develop a platform for a small network. The network consists of the following fixed and mobile nodes that communicate wirelessly using Bluetooth technology: A sensor/actuator node programmable in C programming language with an AVR processor. A programmable mobile phone with Bluetooth capability, programmable in Java2ME. A PC as a fixed node with Bluetooth hardware, to be programmed in Java2SE. Students will be provided with a specification of the system to be developed and will independently prepare all development documentation under supervision. Students will learn to design, program and debug a client/server system. During the internship, students are supported by experienced tutors. The internship is highly structured. Weekly tasks are assigned according to a set schedule and their solutions must be demonstrated on time. |
|
Objectives | Students can systematically develop software for embedded systems. They know the entire development process from specification, through development, to debugging and documentation. The students can use proven development environments such as Eclipse, Netbeans, Subversion and the team communication system TRAC. The practical course is completed in small groups. Emphasis is placed on teamwork, communication within and between groups, systematic problem solving and meeting deadlines. This promotes students' self-confidence, self-marketing skills and ability to deal with conflict. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Bringmann | |
Literature / Other | • M. Sauter. Grundkurs Mobile Kommunikationssysteme |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, ML-CS |
Module Number INFO-4321 |
Module Title Enterprise Computing - Foundation |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | In the winter semester | |
Language of instruction | German | |
Type of Exam | Written Test |
|
Content | The course provides students with an overview of the IT systems and facilities that large corporations and government organizations use to manage their daily IT operations. The content of the module is mainly based on the use of modern mainframe computers and the mainframe technology used. In detail, the course covers the following topics: Overview, Processing Basics, z-Systems Architecture and Hardware, Firmware and RAS, z/OS Operating System, Input/Output Processing, Data Organization, Virtualization and System Management, Unix and Linux on z-Systems, Clustering and Sysplex, IT Infrastructure. |
|
Objectives | The students master the learned system structures and computer architectures as they are used by large companies and can apply them in a problem-oriented manner. This enables the students to apply the advantages and disadvantages of these IT methods to new scenarios in industrial practice in a situation-appropriate manner and to expand them competently. Furthermore, the students can justify and present the necessary system decisions. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Kreißig, Schmidt | |
Literature / Other | • U. Kebschull, P. Herrmann, W.G. Spruth. Einführung in z/OS und OS/390. 2. Auflage, Oldenbourg 2004, ISBN 3-486-27393-0. |
|
Last offered | unknown | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, ML-CS |
Module Number INFO-4322 |
Module Title Enterprise Computing - Practical Course |
Lecture Type(s) Practical Course |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | In the winter semester | |
Language of instruction | German | |
Type of Exam | Elaboration and presentation of the internship tasks |
|
Content | File management under TSO, development of a COBOL batch program, VSAM file organization, DB2 |
|
Objectives | The students are able to apply the knowledge learned in the module Enterprise Computing and to deepen and expand it independently accordingly. They are able to see through, analyse and solve practical problems accordingly. Thus, at the end of their studies, the students are able to process tasks in industrial practice in a result-oriented and competent manner. The tasks set in this module are worked on in small groups. This trains cooperation, communication and conflict skills as well as self-discipline and a sense of responsibility. |
|
Prerequisite for participation | INFO-4321 Enterprise Computing - Foundation | |
Lecturer | Kreißig | |
Literature / Other | • Herrmann, Paul ; Spruth, Wilhelm G.: Einführung in z/OS und OS/390 : Web-Services und Internet-Anwendungen für Mainframes. 3. Aufl. München : Oldenbourg Wissenschaftsverlag, 2012. – ISBN 978-3-48670428-0 |
|
Last offered | unknown | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, ML-CS |
Module Number INFO-4323 |
Module Title Enterprise Computing - Applications |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | In the summer semester | |
Language of instruction | German | |
Type of Exam | Written Test |
|
Content | This course provides students with an overview of the range of applications and the middleware services they require to meet the diverse customer needs of large enterprises and government organizations. The focus of these courses is the description of the different software components and their interaction. In detail, the event covers the following topics: Industrial Mainframe Application and Transaction Processing, CICS, Mainframe Database Management System, System Communication, Analytics and BigData, Secure Container and Blockchain, Workload Management, Java on Mainframes, Web Application Server, Software Development in Large Enterprises. |
|
Objectives | Students learn the requirements for operating complex software environments and applications that large companies and government institutions use to manage their daily operations. This enables them to analyse these software environments and define possible extensions and additions. Furthermore, the students can present and evaluate these concepts to decision-makers. Overall, the students are able to apply the advantages and disadvantages of these IT methods to new scenarios in industrial practice in a situation-appropriate manner and to expand them competently. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Kreißig, Schmidt | |
Literature / Other | • Herrmann, Paul ; Spruth, Wilhelm G.: Einführung in z/OS und OS/390 : Web-Services und Internet-Anwendungen für Mainframes. 3. Aufl. München : Oldenbourg Wissenschaftsverlag, 2012. – ISBN 978-3-48670428-0 |
|
Last offered | unknown | |
Planned for | Sommersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, ML-CS |
Module Number INFO-4324 |
Module Title Enterprise Computing - Applications Practical Course |
Lecture Type(s) Practical Course |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | In the summer semester | |
Language of instruction | German | |
Type of Exam | Elaboration and presentation of the internship tasks |
|
Content | COBOL unter CICS, CICS Transaction Gateway, IBM Rational Developer für z Systems, Java Remote Method Invocation |
|
Objectives | The students are able to apply the knowledge learned in the module Enterprise Computing, to deepen it accordingly and to extend it independently. They can see through, analyse and solve practical problems. Thus, at the end of their studies, the students are able to process tasks in industrial practice in a results-oriented and competent manner. The tasks set in this module are worked on in small groups. In addition to cooperation, communication and conflict skills, this also trains self-discipline and a sense of responsibility. |
|
Prerequisite for participation | INFO-4323 Enterprise Computing - Applications | |
Lecturer | Kreißig | |
Literature / Other | • Herrmann, Paul ; Spruth, Wilhelm G.: Einführung in z/OS und OS/390 : Web-Services und Internet-Anwendungen für Mainframes. 3. Aufl. München |
|
Last offered | unknown | |
Planned for | Sommersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, ML-CS |
Module Number INFO-4374 |
Module Title Software Quality |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | In the summer semester | |
Language of instruction | German | |
Type of Exam | Presentation |
|
Content | The reliability, safety, correctness and robustness of embedded systems are becoming increasingly important. Errors occur again and again, including critical ones that are due to thought-logical errors in the specification and implementation of the system. Hardware and software are as important as the hardware description languages and programming languages used. For the avoidance of errors restrictions are often specified to the used languages, in order to prevent dynamic malfunctions, but also, in order to simplify the analysis and verification. The techniques range from static analysis of systems, programs and specifications with respect to a wide variety of issues to more and more combinations of machine proof systems and model checkers. In addition to fault avoidance, fault tolerance (e.g., through redundancy, multiple design) is also an interesting approach for software. Techniques such as runtime checking, observer processes, monitoring, consistency checking are used. More and more, the focus is on quality testing and guaranteeing properties of the systems. An example would be e.g. the certification of safety-relevant systems. In this connection also libraries, tools, compilers, system components, foreign software are of importance, for which the manufacturers are likewise responsible. Mastering these complex interrelationships is not only relevant for systems that pose a risk to human life and limb, but also in the case of economic risk potential, e.g. in the area of security. The goal of this seminar is to give an insight into the theory of embedded system verification and the current tools developed in research, without losing focus on the industrial methods used today. |
|
Objectives | The students can research scientific literature and have acquired reading and learning skills. They can prepare a topic in a structured manner and present it in writing and in the form of a lecture. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Kropf | |
Literature / Other | aktuelle Veröffentlichungen aus Industrie und Forschung |
|
Last offered | unknown | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, MEDZ-SEM, ML-CS |
Module Number INFO-4661 |
Module Title Computer Engineering (Seminar) |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German | |
Type of Exam | Presentation |
|
Content | Changing topics on technologies and methods from the research-oriented, scientific environment of computer engineering. Please note announcements and notices. |
|
Objectives | Students are able to understand a complex, scientific issue from written sources, process it and present it independently in the form of a lecture with discussion and summarise it in a well-structured paper they have prepared themselves. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | ||
Literature / Other | wird in der Vorbesprechung bekannt gegeben |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, MEDZ-SEM, ML-CS |
Module Number INFO-4399 |
Module Title Advanced Topis in Computer Engineering |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Presentation |
|
Content | This module deals with current topics in the field of technical computer science. These are brought to the students by means of current literature from research and industry. The module is primarily aimed at students who wish to acquire advanced knowledge in this area. |
|
Objectives | The students are able to recognise, describe and evaluate current topics in technical computer science. By working on the topics independently, they have trained self-discipline as well as reading and learning skills of the students. Moderation competence, rhetoric and critical ability of the students are particularly improved by presenting the topic in front of an expert audience. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Bringmann | |
Literature / Other | Aktuelle Literatur, die in der Vorbesprechung bekannt gegeben wird. |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, MEDZ-MEDTECH, MEDZ-SEM, ML-CS |
Module Number INFO-4191 |
Module Title Neuronal Computing |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | In the summer semester | |
Language of instruction | German | |
Type of Exam | Oral examination (written exam if there are a large number of participants) |
|
Content | Within the module Neuronal Computing one of the best organized and most efficient systems to the computer will be presented: the biological neuronal network. In a first step, methods for communication with this computer system will be shown. Starting from information theory, methods for recording neuronal signals and their signal processing will be treated. First, different methods for recording nerve signals and the problems arising with them are treated from the point of view of signal processing. Afterwards methods for signal processing of nerve signals (spike sorter etc.) are presented. In particular, the current methods such as the JPSH (Joint Peri-Stimulus Histogram) or ISC (Inca-SOM-Clusot) will be discussed. The course is divided into Information Theory, Neurons as Computers, Networked Neurons, Recording Techniques, Signal Processing of Neural Signals, Modular/ Population Coding, Unitary Events Analysis, and Applications. |
|
Objectives | The students have a deep scientific insight into neural computing based on current publications. They are able to transfer findings from biological systems and medicine directly into the field of computer science. This transfer performance requires a high degree of reading and learning competence and a high level of commitment to independent scientific information retrieval. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Nagel | |
Literature / Other | aktuelle Veröffentlichungen |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, ML-CS |
Module Number INFO-4192 |
Module Title Machine Learning and Artificial Neuronal Networks in Biomedical Applications |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Presentation |
|
Content | The seminar "Machine Learning and Artificial Neural Networks in Biomedical Applications" deals with current topics in signal processing in the field of nerve signal processing (e.g. neuroprosthetics or brain-computer interfaces), medical signals (e.g. fMRI or MEG) or related areas as well as signal processing algorithms used in these areas. |
|
Objectives | The students have a deep scientific insight into neural computing based on current publications. They are able to transfer findings from biological systems and medicine directly into the field of computer science. This transfer performance requires a high degree of reading and learning competence and a high level of commitment to independent scientific information retrieval. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Nagel | |
Literature / Other | aktuelle Veröffentlichungen |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-VIS, MEDZ-MEDTECH, MEDZ-SEM, ML-CS |
Module Number INFO-4161 |
Module Title Image Processing II (3D-Computer-Vision) |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | In the summer semester | |
Language of instruction | German | |
Type of Exam | Written exam (oral exam if the number of participants is small), grade can be improved by practice points (bonus). Minimum score in exercises required for admission to the exam. |
|
Content | Topics include: Feature Point Extraction, Correlation and Matching, Epipolar Constraint, Fundamental Matrix, Camera Position Calculation, Image Warping, Optical flow and Dense Correspondence Matching. |
|
Objectives | The students know the basic procedures for reconstructing 3D scenes from images and video recordings and can implement them. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Schilling | |
Literature / Other | Vorlesungsfolien werden zum Download bereitgestellt |
|
Last offered | Sommersemester 2022 | |
Planned for | Sommersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-VIS, MEDZ-MEDTECH, ML-CS |
Module Number INFO-4162 |
Module Title Image Processing II (3D-Computer-Vision) |
Lecture Type(s) Practical Course |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | In the winter semester | |
Language of instruction | German | |
Type of Exam | Project, presentation and elaboration |
|
Content | Implementation of programms from the field of computer vision. |
|
Objectives | The students can independently (in small groups) plan and create programmes to solve simple problems of 3D reconstruction from images and apply their theoretical knowledge. |
|
Prerequisite for participation | INFO-4161 Image Processing II (3D-Computer-Vision) | |
Lecturer | Schilling | |
Literature / Other | Entwicklungsumgebung wird zur Verfügung gestellt |
|
Last offered | unknown | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-VIS, MEDZ-MEDTECH, ML-CS |
Module Number INFO-4163 |
Module Title Medical Image Processing |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German | |
Type of Exam | Written exam (in case of a small number of participants: oral tests) |
|
Content | Processing of image and volume data in medicine: imaging techniques, X-ray, CT, MR, PET, radon transformation, filtering of 2D and 3D data, segmentation in 2D and 3D, visualization of voxel-based volume data, atlases and statistical models. |
|
Objectives | The students know the important imaging procedures in medicine and understand the underlying technical and physical processes. They know basic algorithms for further processing and presentation of the acquired data. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Schilling | |
Literature / Other | Vorlesungsfolien werden zum Download bereitgestellt |
|
Last offered | - | |
Planned for | - | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-VIS, MEDZ-MEDTECH, ML-CS |
Module Number INFO-4164 |
Module Title Medical Image Processing |
Lecture Type(s) Practical Course |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German | |
Type of Exam | Project, presentation and elaboration |
|
Content | Implementation of programs from the field of image processing, e.g. segmentation of X-ray data, visualization of voxel data |
|
Objectives | Students can independently (in small groups) plan and implement programmes to solve simple problems in medical image processing, applying their theoretical knowledge. |
|
Prerequisite for participation | INFO-4163 Medical Image Processing | |
Lecturer | Schilling | |
Literature / Other | Entwicklungsumgebung wird zur Verfügung gestellt |
|
Last offered | - | |
Planned for | - | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-VIS, MEDZ-MEDTECH, ML-CS |
Module Number INFO-4170 |
Module Title Geometric Modelling and Simulation |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German | |
Type of Exam | Written exam (oral exam if the number of participants is small), grade can be improved by practice points (bonus). Minimum score in exercises required for admission to the exam. |
|
Content | Generation of polygon meshes, point data processing (laser scanning, registration,... ) Point-based representations, efficient mesh data structures, mesh compression, remeshing, hierarchical structures, mesh simplification. |
|
Objectives | Students know the basic methods and algorithms for optimising, processing and storing geometric data. They are able to implement current algorithms for geometry processing. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Schilling | |
Literature / Other | Eigene Materialien und Vorlesungsfolien werden zum Download bereitgestellt |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-VIS, MEDZ-MEDTECH, ML-CS |
Module Number INFO-4172 |
Module Title Virtual Reality |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German | |
Type of Exam | Written exam (oral exam if the number of participants is small), grade can be improved by practice points (bonus). Minimum score in exercises required for admission to the exam. |
|
Content | Scene graphs, stereo (HW, SW), tracking (HW, SW), acceleration techniques (LOD; culling), collision detection, haptics, sound, GPU programming |
|
Objectives | The students know hardware and software components of current VR systems and have a broad knowledge of algorithms from the areas of acquisition, simulation and rendering that are relevant for VR systems. They are able to implement components of a VR system. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Schilling | |
Literature / Other | - |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-VIS, MEDZ-MEDTECH, ML-CS |
Module Number INFO-4179 |
Module Title Special Topics in Computer Graphics |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German | |
Type of Exam | Written exam (oral exam if the number of participants is small), grade can be improved by practice points (bonus). Minimum score in exercises required for admission to the exam. |
|
Content | Special topics from the field of graphic data processing. |
|
Objectives | The students acquire in-depth knowledge in special areas of graphical data processing, which are important e.g. for doctoral projects in the field of work. They can classify and evaluate new approaches. They are able to independently develop and implement new algorithms in the special field. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Schilling | |
Literature / Other | Vorlesungsfolien werden zum Download bereitgestellt |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-VIS, MEDZ-MEDTECH, ML-CS |
Module Number INFO-4187 |
Module Title Image Processing, Machine Learning and Computer Vision |
Lecture Type(s) Practical Course |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German and English | |
Type of Exam | Project, presentation and elaboration |
|
Content | Implementation of problem solving with algorithms from the fields of image processing, machine learning and computer vision. |
|
Objectives | Students can independently (in small groups) use suitable algorithms and methods from the fields of image processing, machine learning and computer vision to solve concrete problems and combine methods from the different fields. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Schilling | |
Literature / Other | Entwicklungsumgebung wird zur Verfügung gestellt |
|
Last offered | Sommersemester 2022 | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-VIS, MEDZ-MEDTECH, ML-CS |
Module Number INFO-4168 |
Module Title Advanced Topics of Computer Graphics, Computer Vision and Machine Learning |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Presentation and written report |
|
Content | Advanced topics in graphic data processing and computer vision, rendering algorithms, rendering hardware, computer vision and pattern recognition, classification, modeling, learning techniques in computer graphics and computer vision. |
|
Objectives | Students are able to develop an advanced topic from the field of graphical data processing on the basis of current conference papers and journal articles, present and discuss it in front of the group and present the essentials in an understandable and correct way in a written paper. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Lensch, Schilling | |
Literature / Other | Hängen von den aktuellen Themen ab und werden zur Verfügung gestellt |
|
Last offered | unknown | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-VIS, MEDZ-SEM, ML-CS, ML-DIV |
Module Number MEDI-4510 |
Module Title Audiovisual Media I (Camera and Digital Editing) |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | In the winter semester | |
Language of instruction | German | |
Type of Exam | Workpiece with documentation |
|
Content | In the module, students first learn how to use professional video cameras in a practical way and deal with central issues of image and lighting design. Following digital video editing techniques using Adobe Premiere. they learn digital video editing techniques using Adobe Premiere. The content includes: integration of static and moving images, title generation, cross-fade effects, use of keyframes, encoding and file formats, file export. |
|
Objectives | Students have knowledge of the basic techniques of digital video editing as well as the basics of using a video camera and lighting technology. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Schilling | |
Literature / Other | Katz: Die richtige Einstellung. Das Lehrbuch über Bildsprache und Filmgestaltung, |
|
Last offered | unknown | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, MEDI-PRAX, MEDZ-SEM, ML-CS |
Module Number MEDI-4511 |
Module Title Audiovisual Media II (3D-Animation) |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | In the winter semester | |
Language of instruction | German | |
Type of Exam | Workpiece with documentation |
|
Content | In the module, students learn basic 3D animation techniques. The focus is on 3D modeling and key frame animation. Common 3D animation software is used. Students learn the basics of animated film and technical animation in a very practical way using their own 3D workpieces. The certificate of achievement consists of a short 3D animation film, which the students create in small groups. |
|
Objectives | Students have knowledge of basic 3D animation techniques, in particular 3D modelling and keyframe animation. They can actively implement this knowledge with common 3D animation software. The students acquire this knowledge in a practical way using their own workpieces. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Schilling | |
Literature / Other | Flavell: Beginning Blender: Open Source 3D Modeling, Animation, and Game Design, |
|
Last offered | unknown | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, MEDI-PRAX, MEDZ-SEM, ML-CS |
Module Number MEDI-4512 |
Module Title Audiovisual Media III (Special Effects) |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German | |
Type of Exam | Workpiece with documentation |
|
Content | Students work out and discuss central special effects and trick techniques of film productions on the basis of current examples. At the same time, they learn basic special effects, which they are to implement in small groups in form of a short film. Examples of effects include: Bluescreen/Greenscreen, overlay, digital matte, miniature effects, stop motion. |
|
Objectives | Students have knowledge and in-depth understanding of the basic special effects of film production and are familiar with current effects techniques based on relevant, current examples. |
|
Prerequisite for participation | MEDI-4510 Audiovisual Media I (Camera and Digital Editing) | |
Lecturer | Schilling | |
Literature / Other | Fontaine: Adobe After Effects CS5: Das Praxisbuch zum Lernen und Nachschlagen, |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, MEDI-PRAX, MEDZ-SEM, ML-CS |
Module Number MEDI-4599 |
Module Title Media Production |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | In the summer semester | |
Language of instruction | German | |
Type of Exam | Written elaboration and presentation based on it |
|
Content | Changing topics from the field of media production are presented by external lecturers from media and companies and introduced through practical projects. Examples are the topics typography and layout or sound engineering. |
|
Objectives | Students possess in-depth knowledge and skills in a specific area of media production. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Schilling | |
Literature / Other | Aktuelle Literatur, die in der Veranstaltung bekannt gegeben wird. |
|
Last offered | Sommersemester 2022 | |
Planned for | Sommersemester 2024 | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, MEDI-PRAX, MEDZ-SEM, ML-CS |
Module Number INFO-4465 |
Module Title Lambda Calculus and Combinatorial Logic |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Written Test |
|
Content | inhalt 4465 |
|
Objectives | goals 4465 |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Piecha | |
Literature / Other | - |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS |
Module Number INFO-4492 |
Module Title Special Topics in Learning Theory |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German and English | |
Type of Exam | Written exam (in case of a small number of participants: oral tests) |
|
Content | In this module we discuss advanced results and approaches in learning theory |
|
Objectives | Students get to know about advanced results in learning theory. They can judge |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | von Luxburg | |
Literature / Other | will be announced in the lecture |
|
Last offered | - | |
Planned for | - | |
Assigned Study Areas | INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV |
Module Number INFO-4493 |
Module Title Learning Theory |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German and English | |
Type of Exam | Presentation and written report |
|
Content | In this seminar we discuss current research papers in the area of machine learning |
|
Objectives | Students are able to read and reflect upon current research papers in the area |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | von Luxburg | |
Literature / Other | will be announced in the lecture / Basic knowledge in machine learning. |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS, ML-DIV |
Module Number MEDI-4310 |
Module Title Advanced Web-Engineering |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German | |
Type of Exam | Written exam (in case of a small number of participants: oral tests) |
|
Content | Content of this module are methods and techniques for the development of complex web applications. On the one hand, the possibilities of JEE are covered, first the basic approaches of servlets and JSP, then EJB, JSF and Hypernate. Furthermore, the possibilities of the dotnet framework based on C# will be covered. In addition to the pure development of advanced web applications, their operation in the corresponding server structures as well as the evaluation of the approaches with regard to stability, performance and development effort are also covered. |
|
Objectives | Students have knowledge of the techniques for developing advanced web applications, especially in JEE framework and dotnet. They are able to independently design and implement advanced web applications, assessing and actively implementing different design patterns. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Walter | |
Literature / Other | T. Walter: Kompendium der Web-Programmierung, Springer, 2008 |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-WEB, ML-CS |
Module Number MEDI-4320 |
Module Title Advanced Media Application in the Internet |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Written exam (oral exam if the number of participants is small) 80%, exercises 20% |
|
Content | Methods of digital audio and video generation, detectors, physical basics, sampling, encoding methods, hardware accelerated encoding, multimedia transmission protocols, IP-based multimedia transmission protocols, media data transmission security, basics of media players, integration of audio and video in web applications - WebRTC, web sockets, html5. Media container formats - mp3, mp4. Practical components: Video conferencing in daily use, the lecture hall as a video studio, the realtime and on demand media server system of the University of Tübingen TIMMS. |
|
Objectives | Students will understand and learn basic and advanced concepts and procedures of audio and video information generation, encoding, transmission and computer-aided presentation. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Walter | |
Literature / Other | Grundkenntnisse der Protokolle des Internet-Protokol-Stacks und der Netzwerkprogrammierung, Grundkenntnisse einer objektorientierten Programmiersprache |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-WEB, ML-CS |
Module Number MEDI-4330 |
Module Title Digital Photography for the Web |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German | |
Type of Exam | Written exam (oral exam if the number of participants is small) 80%, exercises 20% |
|
Content | The content of this module is the principles of analog and digital photography. This includes in particular the physical and chemical fundamentals. Building on this, the digitization of the image and the various possible formats for the web as well as basic image processing are covered. Bayer mosaic resolution for color will be discussed. Digital watermarks (robust and fragile) and forms of presentation on the web are topics of the module and legal aspects. |
|
Objectives | The students understand the physical principles of analogue and especially digital photography and can implement the common techniques. They are actively familiar with image processing in general and especially for the target platform web, as well as the algorithmic resolution of the Bayer Mosaic. They can answer basic legal questions (copyright, image rights). |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Walter | |
Literature / Other | T. Walter: MediaFotografie, Springer, 2005 |
|
Last offered | unknown | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-PRAX, MEDI-WEB, ML-CS |
Module Number MEDI-4399 |
Module Title Selected Topics in Web and Internet |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German | |
Type of Exam | Written exam (in case of a small number of participants: oral tests) |
|
Content | Changing topics from the field of web development and multimedia. Exercises are held in small groups. |
|
Objectives | Competences: Students possess in-depth knowledge and skills in a specific area of web development and multimedia from current research and development. They can actively realise these skills conceptually and in implementation. They can actively present their solutions. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Walter | |
Literature / Other | Aktuelle Literatur, die in der Veranstaltung bekannt gegeben wird. |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-WEB, ML-CS |
Module Number INFO-4151 |
Module Title Applied Statistics II |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | In the summer semester | |
Language of instruction | German | |
Type of Exam | Written Test |
|
Content | Building on "Applied Statistics I" (german: "Angewandte Statistik I"), more complex statistical methods are covered: Generalized Linear Models (GLM), Principal Component Analysis (PCA), Independence Analysis (ICA), and Bayesian statistics. The emphasis is on the practical application of all methods and their implementation in Python (with the modules statsmodels, scipy.stats, sklearn and pymc) and the presentation of results in notebooks. |
|
Objectives | The students know advanced statistical methods, how to use them and how to implement them in software. They can figure out the differences between frequentist and Bayesian statistics. |
|
Prerequisite for participation |
INF3223 Applied Statistics I, INFM1010 Mathematics for Computer Science 1: Analysis, INFM1020 Mathematics for Computer Science 2: Linear Algebra |
|
Lecturer | Wannek | |
Literature / Other | Wird in der Vorlesung bekannt gegeben |
|
Last offered | Sommersemester 2022 | |
Planned for | Sommersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, ML-CS |
Module Number INFO-4166 |
Module Title Psychophysical Methods |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | In the winter semester | |
Language of instruction | English | |
Type of Exam | Written Test |
|
Content | The lecture covers the following topics: |
|
Objectives | Students learn the central behavioural limits, concepts and psychophysical methods in sensory psychology. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Wichmann | |
Literature / Other | Literatur / Literature: Teilnahmevoraussetzungen / Prerequisites: |
|
Last offered | Wintersemester 2022 | |
Planned for | Wintersemester 2023 | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-VIS, ML-CS |
Module Number INFO-4169 (MKOGW1) |
Module Title Sensory Psychology |
Lecture Type(s) Lecture, Seminar |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 2 Semester | |
Frequency | Each semester | |
Language of instruction | English | |
Type of Exam | Written exam (Lecture), Oral presentation (seminar) |
|
Content | The module consists of 2 parts: To complete the module, students have to take the compulsory lecture "Psychophysical Methods" first (in the winter term), followed by one of the three seminars (in the summer term). The contents of the lecture and the seminars are: 2a) Seminar "Spatial Vision": Optics of the eye, absolute thresholds, adaptation, contrast sensitivity function, spatial frequency selectivity, contrast gain-control, early visual representation of the world. 2b) Seminar "Colour Vision & Material Perception": Spectral composition of light, wavelength encoding, colour matching, trichromacy, colour appearance, colour constancy, material properties & perception. 2c) Seminar "Theories of Vision": Inverse optics, Gibson’s direct perception, vision as (unconscious) inference, the interface theory of vision, vision as predictive coding, the efficient coding hypothesis. |
|
Objectives | The students can classify and critically reflect the central behavioural limits, concepts and psychophysical methods in sensory psychology. They have in-depth knowledge of the state-of-the-art models and their theoretical foundations in one specific subdomain of sensory psychology (spatial vision, |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Wichmann | |
Literature / Other | Literatur / Literature: Wird zu Beginn jeder Veranstaltung angekündigt. / Will be announced at the beginning of each course. Teilnahmevoraussetzungen / Prerequisites: Grundlagenwissen in Mathematik und Statistik; dringend empfohlen wird zudem Grundlagenwissen über die Prinzipien visueller Wahrnehmung. / Basic knowledge in mathematics and statistics is required; basic knowledge of the fundamentals of visual perception is strongly recommended. |
|
Last offered | Wintersemester 2022 | |
Planned for | Sommersemester 2023 | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-VIS, MEDZ-SEM, ML-CS |
Module Number ML-4710 |
Module Title Beyond Fairness: a Socio-Technical view of Machine Learning |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Written Test |
|
Content | inhalt ml 4710 |
|
Objectives | goals ml 4710 |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Williamson | |
Literature / Other | - |
|
Last offered | - | |
Planned for | - | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV |
Module Number INFO-4181 |
Module Title Pattern Recognition and Machine Learning |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German and English | |
Type of Exam | To be announced. |
|
Content | The module covers the first chapters of the textbook by Ch. Bishop mentioned below: Introduction to Machine Learning, probability distributions, linear models for regression, linear models for classification, neural networks (short), kernel methods, mixture models and EM algorithms. |
|
Objectives | Students acquire knowledge about machine learning on a modern statistical basis. They know mathematical-statistical approaches for solving pattern recognition problems and can apply them in exercises. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Zell | |
Literature / Other | Ch. Bishop: Pattern Recognition and Machine Learning, Springer-Verlag; |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-VIS, ML-CS |
Module Number INFO-4183 |
Module Title Evolutionary Algorithms |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German and English | |
Type of Exam | To be announced. |
|
Content | Outline and systematics of heuristic optimization methods, genetic algorithms, classifier systems, genetic programming, evolution strategies, multicriteria optimization, swarm algorithms. In the accompanying exercises, participants deepen the theory or solve simple optimization problems with the optimization system EvA2 and their own programs. |
|
Objectives | The students know the theory and application of modern evolutionary algorithms (genetic algorithms, evolutionary strategies, genetic programming, swarm algorithms, etc.). They can select the optimal algorithms for the respective problem and solve optimisation problems with them. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Zell | |
Literature / Other | Skriptum zur Vorlesung |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, ML-CS |
Module Number INFO-4184 |
Module Title Evolutionary Algorithms |
Lecture Type(s) Practical Course |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German and English | |
Type of Exam | To be announced. |
|
Content | In teams of about three students, students become familiar with the evolutionary optimization system EvA2 and its optimization procedures, and then solve a real-world complex optimization problem in a team. |
|
Objectives | Students have basic knowledge of evolutionary algorithms from the lecture and can apply these to a larger real-world problem. They master problem analysis, teamwork, time management, documentation and presentation techniques. |
|
Prerequisite for participation | INFO-4183 Evolutionary Algorithms | |
Lecturer | Zell | |
Literature / Other | Wird in der Vorbesprechung ausgeteilt. |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, ML-CS |
Module Number INFO-4361 |
Module Title Mobile Robots |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | In the winter semester | |
Language of instruction | German and English | |
Type of Exam | To be announced. |
|
Content | The Mobile Robots module focuses in particular on wheel-driven robots and flying robots: introduction, mathematical foundations, kinematic modeling of wheel-driven mobile robots, sensors for mobile robots, mapping, localization, navigation of mobile robots, modeling of flying robots |
|
Objectives | The students have acquired knowledge about mobile robots. They can describe the kinematics of mobile robots. They know algorithms for self-localisation, navigation, search and path planning. Furthermore, they know sensors for mobile robots and their properties and know their advantages and disadvantages for different tasks. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Zell | |
Literature / Other | Skriptum Mobile Roboter (Zell), weitere Lit. wird zu Beginn der Vorlesung bekanntgegeben |
|
Last offered | Sommersemester 2022 | |
Planned for | Sommersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, ML-CS, ML-DIV |
Module Number INFO-4362 |
Module Title Mobile Robots |
Lecture Type(s) Practical Course |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German and English | |
Type of Exam | To be announced. |
|
Content | Using outdoor robots based on 1/10 model monster trucks with camera, sonar sensors and laser scanner, tasks such as wall tracking, control, following behavior, self-localization, map building or search algorithms are implemented on mobile robots. The last task usually has a competitive character between the teams. |
|
Objectives | The students can work out problems of sensor technology, control, self-localisation and navigation of mobile robots independently in small groups. They have acquired competences in the areas of problem-solving behaviour, teamwork, time management, programming skills and presentation skills. |
|
Prerequisite for participation | INFO-4361 Mobile Robots | |
Lecturer | Zell | |
Literature / Other | Literatur wird zu Beginn des Praktikums bekanntgegeben bzw. im Praktikum ausgeteilt |
|
Last offered | Wintersemester 2022 | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, ML-CS, ML-DIV |
Module Number INFO-4363 |
Module Title Advanced Topics in Mobile Robots |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | To be announced. |
|
Content | The seminar covers annually changing advanced topics in mobile robotics, e.g. robot kinematics, modern probabilistic methods of navigation and self-localization, mapping, path planning with moving obstacles, robot formations, simultaneous localization and mapping (SLAM), visual self-localization, sensor fusion with different sensors. Sensors. In contrast to the similar proseminar mentioned above, the topics, algorithms and math/physics descriptions are more demanding and the treatment is more in-depth. |
|
Objectives | Students can scientifically analyse a topic from the field of mobile robots, present it and elaborate on it in a paper. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Zell | |
Literature / Other | Literatur wird in der Vorbesprechung angegeben. |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, MEDZ-SEM, ML-CS, ML-DIV |
Module Number INFO-4364 |
Module Title Flying Robots |
Lecture Type(s) Practical Course |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | To be announced. |
|
Content | Using 6+2 newly built flying robots (quadrocopters) with RGBD camera, onboard PC Odroid XU4 and autopilot hardware Pixhawk and a stationary IR tracking system, tasks such as simple flight control, autonomous hover, takeoff and landing, visual odometry, etc. are implemented. Most tasks will be performed first in a simulation system, then with real quadrocopters in an area separated by capture nets separated area of the large robotics laboratory. |
|
Objectives | The students independently develop and implement algorithms for sensor data evaluation, flight control, self-localisation and visual odometry of flying robots in small groups. They acquire competences in the areas of problem-solving behaviour, teamwork, time management, programming skills and presentation skills. |
|
Prerequisite for participation | INFO-4361 Mobile Robots | |
Lecturer | Zell | |
Literature / Other | Literatur wird zu Beginn des Praktikums bekanntgegeben bzw. im Praktikum ausgeteilt |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, ML-CS, ML-DIV |
Module Number INFO-4365 |
Module Title Deep Convolutional Neural Networks |
Lecture Type(s) Practical Course |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | To be announced. |
|
Content | Using modern CUDA-based systems (PCs with Nvidia graphics processors as well as CUDA workstations with 4 GeForce Titan X graphics cards) and modern deep neural network training software, such as Caffe, CNTK or Torch, deep neural network machine learning problems for image classification, object recognition in images and object segmentation are investigated. Here, commonly available benchmark datasets such as NIST, Imageview, etc. are used, but also databases of RGB-D images (images with depth information, such as from the MS Kinect). |
|
Objectives | The students can work out problems of programming, data preprocessing, structure selection of neural networks, training, validation and testing of deep neural networks independently in small groups. They have acquired competences in the areas of problem-solving behaviour, teamwork, time management, programming skills and presentation skills. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Zell | |
Literature / Other | Literatur wird zu Beginn des Praktikums bekanntgegeben bzw. im Praktikum ausgeteilt |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS |
Module Number INFO-4366 |
Module Title Advanced Topics in Neural Networks |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German and English | |
Type of Exam | To be announced. |
|
Content | The seminar deals with yearly changing topics of artificial neural networks, e.g. |
|
Objectives | Advanced Topics in Neural Networks: Students can scientifically analyse a topic from the field of mobile robots, present it and elaborate on it in a paper. |
|
Prerequisite for participation | INF3154 Introduction to Neural Networks | |
Lecturer | Zell | |
Literature / Other | Literatur wird in der Vorbesprechung angegeben. |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS, ML-DIV |
Module Number ML-4320 |
Module Title Time Series |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Written exam (in case of a small number of participants: oral tests) |
|
Content | A time series is an extremely wide-spread type of empirical data: a (potentially |
|
Objectives | Students develop an understanding for key algorithmic and modelling challenges |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Hennig, Ludwig | |
Literature / Other | Literature will be listed at the beginning of the semester. |
|
Last offered | Sommersemester 2020 | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV |
Module Number ML-4101 |
Module Title Mathematics for Machine Learning |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 9 | |
Work load - Contact time - Self study |
Work load:
270 h Class time:
90 h / 6 SWS Self study:
180 h |
|
Duration | 1 Semester | |
Frequency | In the winter semester | |
Language of instruction | English | |
Type of Exam | Written exam (in case of a small number of participants: oral tests) |
|
Content | The lecture will repeat and introduce basic notions of mathematics used in machine learning |
|
Objectives | Students learn the mathematical foundations for the latter machine learning courses. In particular, |
|
Prerequisite for participation |
INFM1010 Mathematics for Computer Science 1: Analysis, INFM1020 Mathematics for Computer Science 2: Linear Algebra, INFM2010 Mathematics for Computer Science 3: Advanced Topics |
|
Lecturer | Hein, Pons-Moll, von Luxburg | |
Literature / Other | The literature for this lecture will be provided at the beginning of the semester. |
|
Last offered | Wintersemester 2022 | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV |
Module Number ML-4201 |
Module Title Statistical Machine Learning |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 9 | |
Work load - Contact time - Self study |
Work load:
270 h Class time:
90 h / 6 SWS Self study:
180 h |
|
Duration | 1 Semester | |
Frequency | In the summer semester | |
Language of instruction | English | |
Type of Exam | Written exam (in case of a small number of participants: oral tests) |
|
Content | The focus of this lecture is on algorithmic and theoretical aspects of statistical |
|
Objectives | ML- 4201 Students get to know the most important classes of statistical machine learning |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Hein, von Luxburg | |
Literature / Other | The literature for this lecture will be provided at the beginning of the semester. / Students need to know the contents of the basic math classes, in particular |
|
Last offered | Sommersemester 2022 | |
Planned for | Sommersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV, ML-FOUND |
Module Number ML-4303 |
Module Title Convex and Nonconvex Optimization |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 9 | |
Work load - Contact time - Self study |
Work load:
270 h Class time:
90 h / 6 SWS Self study:
180 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Written exam (in case of a small number of participants: oral tests) |
|
Content | Convex optimization problems arise quite naturally in many application areas like signal processing, machine learning, image processing, communication and networks and finance etc. The course will give an introduction into convex analysis, the theory of convex optimization such as duality theory, algorithms for solving convex optimization problems such as interior point methods but also the basic methods in general nonlinear unconstrained minimization, and recent first-order methods in non-smooth convex optimization. We will also cover related non-convex problems such as d.c. (difference of convex) programming, biconvex optimization problems and hard combinatorial problems and their relaxations into convex problems. While the emphasis is given on mathematical and algorithmic foundations, several example applications together with their modeling as optimization problems will be discussed. The course requires a good background in linear algebra and multivariate calculus, but no prior knowledge in optimization is required. |
|
Objectives | Students learn the foundations of convex analysis and how to formulate and transform optimization problems. After the lecture they know a variety of methods for solving convex and non-convex optimization problems and have guidelines which method to choose for which problem. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Hein | |
Literature / Other | The lecture does not follow a specific book. The literature for this lecture will be provided at the beginning of the semester. |
|
Last offered | Sommersemester 2022 | |
Planned for | currently not planned | |
Assigned Study Areas | INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV |
Module Number ML-4302 |
Module Title Statistical Learning Theory |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Written exam (in case of a small number of participants: oral tests) |
|
Content | Part 1: basic results in statistical learning theory: |
|
Objectives | Students get to know the standard tools and approaches in statistical learning |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | von Luxburg | |
Literature / Other | The literature for this lecture will be provided at the beginning of the semester. / Students need to know the contents of the basic math classes, in particular |
|
Last offered | - | |
Planned for | - | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV |
Module Number ML-4202 |
Module Title Probabilistic Machine Learning (Probabilistic Inference and Learning) |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 9 | |
Work load - Contact time - Self study |
Work load:
270 h Class time:
90 h / 6 SWS Self study:
180 h |
|
Duration | 1 Semester | |
Frequency | In the summer semester | |
Language of instruction | English | |
Type of Exam | Written exam (in case of a small number of participants: oral tests) |
|
Content | Probabilistic inference is a foundation of scientific reasoning, statistics, and |
|
Objectives | Students gain an intuitive, as well as a mathematical and algorithmic understanding |
|
Prerequisite for participation | ML-4101 Mathematics for Machine Learning | |
Lecturer | Hennig, Macke | |
Literature / Other | Literature will be listed at the beginning of the semester. / Standard undergraduate knowledge of mathematics is required, to the extent |
|
Last offered | Sommersemester 2022 | |
Planned for | Sommersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV, ML-FOUND |
Module Number ML-4301 |
Module Title Numerics of Machine Learning (Numerical Algorithms of Machine Learning) |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Written exam (in case of a small number of participants: oral tests) |
|
Content | The computational cost of machine learning is almost entirely caused by numerical |
|
Objectives | Students develop both an intuitive and mathematical understanding of numerical |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Hennig | |
Literature / Other | Literature will be listed at the beginning of the semester. / Linear algebra is a core theme. Knowledge of probabilistic machine learning |
|
Last offered | Wintersemester 2022 | |
Planned for | --- | |
Assigned Study Areas | INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV |
Module Number ML-4310 |
Module Title Data Mining and Probabilistic Reasoning |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | In the winter semester | |
Language of instruction | English | |
Type of Exam | Written exam (in case of a small number of participants: oral tests) |
|
Content | The lecture gives an introduction into the basics of probability theory, statistics, |
|
Objectives | (1) The students acquire extensive knowledge in theory and application of |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Kasneci G | |
Literature / Other | Will be supplied (book chapters and papers in English) |
|
Last offered | Wintersemester 2022 | |
Planned for | --- | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV |
Module Number ML-4102 |
Module Title Data Literacy |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | In the winter semester | |
Language of instruction | English | |
Type of Exam | Written exam (in case of a small number of participants: oral tests) |
|
Content | This course equips students with concepts and tools that should be familiar to anyone working with (large) data. Based on practical experiments and examples, frequently encountered pitfalls and problems are discussed alongside best practices. We encounter basic statistical notions and problems of bias, testing and experimental design. Foundational methods of machine learning and statistical data analysis are employed to employ these ideas in practice. We will also discuss best practices for scientific data presentation and documentation—how to make expressive figures and tables and perform reproducible experiments—and explore ethical and technical considerations in the context of fairness and transparency. |
|
Objectives | Students develop a sensitivity for common problems and misconceptions in empirical work with data. They understand the mathematical, epistemological, ethical, technical and social challenges surrounding the use of data, and know best practices to address them. They also collect a concrete box of software tools to collect, document, explore, visualize, and draw conclusions from structured, large, small, corrupted and expensive data. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Hennig, Macke | |
Literature / Other | Literatur / Literature: Wird zu Beginn des Semesters mitgeteilt. / Will be listed at the beginning of the semester. Teilnahmevoraussetzungen / Course prerequisties: Grundlegende Kenntnisse in Mathematik und Programmierkenntnisse, wie bspw. durch einen B.Sc. in Informatik erworben. / Only basic math and coding skills as provided by the BSc Computer Science. |
|
Last offered | Wintersemester 2022 | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV |
Module Number ML-4103 |
Module Title Deep Learning (formerly: Deep Neural Networks; INFO-4182) |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | In the winter semester | |
Language of instruction | English | |
Type of Exam | Written exam |
|
Content | Within the last decade, deep neural networks have emerged as an indispensable tool in many areas of artificial intelligence including computer vision, computer graphics, natural language processing, speech recognition and robotics. This course will introduce the (practical and theoretical) principles of deep neural Course Website: https://uni-tuebingen.de/de/175884 |
|
Objectives | Students gain an understanding of the theoretical and practical concepts of deep neural networks including optimization, inference, architectures and applications. After this course, students should be able to develop and train deep neural networks, reproduce research results and conduct original research in this area. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Geiger, Zell | |
Literature / Other | Related literature will be listed throughout the lecture. |
|
Last offered | Wintersemester 2022 | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV, ML-FOUND |
Module Number ML-4340 |
Module Title Self-Driving Cars |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 9 | |
Work load - Contact time - Self study |
Work load:
270 h Class time:
90 h / 6 SWS Self study:
180 h |
|
Duration | 1 Semester | |
Frequency | In the winter semester | |
Language of instruction | English | |
Type of Exam | Written exam |
|
Content | Within the last years, driverless cars have emerged as one of the major workhorses in the field of artificial intelligence. Given the large number of traffic fatalities, the limited mobility of elderly and handicapped people as well as the increasing problem of traffic jams and congestion, self-driving cars promise a solution to one of our societies most important problems: the future of mobility. However, making a car drive on its own in largely unconstrained Course Website: https://uni-tuebingen.de/de/123611 |
|
Objectives | Students develop an understanding of the capabilities and limitations of stateof-the-art autonomous driving solutions. They gain a basic understanding of the entire system comprising perception, learning and vehicle control. In addition, they are able to implement and train simple models for sensori-motor control. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Geiger | |
Literature / Other | Related literature will be listed throughout the lecture. |
|
Last offered | Wintersemester 2022 | |
Planned for | Wintersemester 2025 | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, INFO-TECH, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV |
Module Number ML-4380 |
Module Title Advanced Topics in Machine Learning |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Written exam (oral exam if the number of participants is small), successful participation in the exercise is considered a prerequisite for the exam, the lecturer decides on a bonus. |
|
Content | The module provides an overview of advanced machine learning concepts and applications. Specific topics include the so-called core methods for extracting and analyzing nonlinear features from complex data, optimization methods for extremely large data sets, learning methods for sequential and structured data and security and confidentiality aspects of data analysis. |
|
Objectives | Students have basic knowledge of machine learning on a modern statistical basis. They know mathematical statistical approaches for solving pattern recognition problems and can apply them in exercises. A further prerequisite is sound mathematical knowledge, especially in linear algebra, statistics and analysis. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Alle Dozenten | |
Literature / Other | J. Shawe-Taylor and N. Cristianini: Kernel Methods for Pattern Analysis. Cambridge University Press, 2004. Skript in englischer Sprache |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV |
Module Number ML-4350 |
Module Title Reinforcement Learning |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | In the summer semester | |
Language of instruction | English | |
Type of Exam | Presentation and written report (or exam, will be determined) |
|
Content | The lecture covers the whole range of reinforcement learning topics, from the basic formalism and theory to state-of-the-art algorithms. • Introduction to supervised learning and optimization |
|
Objectives | (1) Students can phrase a problem in the reinforcement learning framework and |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Martius | |
Literature / Other | Reinforcement learning by Sutton and Barto http://incompleteideas.net/ |
|
Last offered | Wintersemester 2021 | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV |
Module Number ML-4410 |
Module Title Neural Data Analysis |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | In the summer semester | |
Language of instruction | English | |
Type of Exam | Written report and cumulative oral exam |
|
Content | In recent years experimental methods to record brain activity have been revolutionized. |
|
Objectives | (1) In this course students will acquire knowledge of basic and advanced techniques |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Berens | |
Literature / Other | Emery N Brown, Robert E Kass, und Partha P Mitra, „Multiple neural spike |
|
Last offered | --- | |
Planned for | --- | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV |
Module Number ML-4420 |
Module Title Efficient Machine Learning in Hardware |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | In the summer semester | |
Language of instruction | English | |
Type of Exam | Oral Test |
|
Content | The recent breakthroughs in using deep neural networks for a large variety of |
|
Objectives | The students gain in-depth knowledge about the challenges associated with |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Bringmann | |
Literature / Other | Will be announced in the first lecture / Knowledge about foundations in machine learning |
|
Last offered | Sommersemester 2022 | |
Planned for | Sommersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, ML-CS, ML-DIV |
Module Number ML-4501 |
Module Title Machine Learning Seminar |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Presentation and written report |
|
Content | In this module we discuss advanced results and approaches in machine learning theory and application and current research results in the area of machine learning in general. Please refer to the curse catalogue in alma to see which specific courses are offered in a respective semester. |
|
Objectives | Students get to know about advanced results in machine learning theory and applications. They can judge for example whether an algorithm is well designed, both from an algorithmic and statistical point of view. They understand about the fundamental limitations of machine learning. They can reflect current research questions. Students will be able to acquire knowledge about current findings through comprehensive literature search. They will know the importance of current topics in the area of machine learning, and will be aware that there are still many open questions. Students will not only have improved their study and reading skills, but will also have enhanced their capability of working independently. The teaching method in this seminar aims at boosting the students’ confidence (oral presentation), and at enhancing their communication skills and enabling them to accept criticism (discussion session following their presentation. After this module they are well-prepared to write a master thesis in the area of machine learning. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Alle Dozenten | |
Literature / Other | Will be handed out in the course |
|
Last offered | Wintersemester 2022 | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS, ML-DIV |
Module Number ML-4520 |
Module Title Ethics and Philosophy of Machine Learning |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Presentation and written report |
|
Content | The fact that machine learning algorithms will play a central role in the process |
|
Objectives | Students can reflect current research questions in the area of philosophy and |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Ethics Lab, Genin, Grothe | |
Literature / Other | Will be handed out in the course |
|
Last offered | Sommersemester 2022 | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS, ML-DIV |
Module Number ML-4510 |
Module Title Practical Machine Learning |
Lecture Type(s) Practical Course |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Oral presentation, written report, lab journal |
|
Content | The practical course consists of finishing assigned tasks in small teams, autonomously |
|
Objectives | Students will gain practical experience in designing and programming methods |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Alle Dozenten | |
Literature / Other | - |
|
Last offered | - | |
Planned for | - | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV |
Module Number ML-4998 |
Module Title Research Project Machine Learning |
Lecture Type(s) Research Project |
---|---|---|
ECTS | 9 | |
Work load - Contact time - Self study |
Work load:
270 h Class time:
90 h / 6 SWS Self study:
180 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Term Paper |
|
Content | The research project serves to deepen theoretical and practical knowledge in a |
|
Objectives | Machine Learning research project: The students |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Dozenten/Arbeitsgruppenleiter | |
Literature / Other | Scientific literature/publications relevant to the research topic to be addressed / Excellent academic grades in Master Machine Learning. There are only a few |
|
Last offered | - | |
Planned for | - | |
Assigned Study Areas | ML-CS, ML-DIV |
Module Number MEDZ-4610 |
Module Title Medical techniques |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 9 | |
Work load - Contact time - Self study |
Work load:
270 h Class time:
90 h / 6 SWS Self study:
180 h |
|
Duration | 1 Semester | |
Frequency | In the winter semester | |
Language of instruction | English | |
Type of Exam | Written Test |
|
Content | The following courses are to be attended: The module handbook M.Sc. Biomedical Technologies for the current semester can be found at \url{http://www.medizin.uni-tuebingen.de/Studierende/Medizintechnik/Masterstudiengang+_Biomedical+Technologies_-port-10011-p-66480.html}. |
|
Objectives | The exact qualification objectives can be found in the module handbook M.Sc. Biomedical Technologies. This can be found for the respective current semester at \url{http://www.medizin.uni tuebingen.de/Studierende/Medizintechnik/Masterstudiengang _Biomedical+Technologies_-port-10011-p-66480.html}. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Dozenten der Medizintechnik | |
Literature / Other | - |
|
Last offered | - | |
Planned for | - | |
Assigned Study Areas | INFO-INFO, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDZ-MEDTECH, ML-CS |
Module Number MEDZ-4620 |
Module Title Biorobotics |
Lecture Type(s) Lecture, Seminar |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Written Test |
|
Content | inhalt mz 4621 |
|
Objectives | goals mz 4620 |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Häufle | |
Literature / Other | - |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, MEDZ-MEDTECH, MEDZ-SEM, ML-CS |
Module Number ML-4210 |
Module Title Advanced Probabilistic Machine Learning Modeling and Applications |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Written Test |
|
Content | inhalt ml 4210 |
|
Objectives | goals ml 4210 |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | De Bacco | |
Literature / Other | - |
|
Last offered | - | |
Planned for | - | |
Assigned Study Areas | INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV |
Module Number ML-4601 |
Module Title Introduction to Game Theory with Application to Multi-Agent Systems |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 9 | |
Work load - Contact time - Self study |
Work load:
270 h Class time:
90 h / 6 SWS Self study:
180 h |
|
Duration | 1 Semester | |
Frequency | In the winter semester | |
Language of instruction | English | |
Type of Exam | Written Test |
|
Content | This module is about game theory and mechanism design, with an emphasis on |
|
Objectives | After the lectures, the students have a broad and profound knowledge of essential |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Maghsudi | |
Literature / Other | • Mas-Colell and M.D. Whinston, and J.R. Green, Microeconomic Theory, |
|
Last offered | Wintersemester 2022 | |
Planned for | Wintersemester 2023 | |
Assigned Study Areas | INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV |
Module Number ML-4502 |
Module Title Machine learning methods for scientific discovery |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Oral presentation, written report |
|
Content | In this seminar, we will discuss current and classical research papers which |
|
Objectives | Students are able to read and reflect upon current research papers in this |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Macke | |
Literature / Other | Will be announced in the first meeting / Basic knowledge probabilistic machine learning |
|
Last offered | Wintersemester 2021 | |
Planned for | Sommersemester 2023 | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS, ML-DIV |
Module Number ML-4360 |
Module Title Computer Vision |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 9 | |
Work load - Contact time - Self study |
Work load:
270 h Class time:
90 h / 6 SWS Self study:
180 h |
|
Duration | 1 Semester | |
Frequency | In the summer semester | |
Language of instruction | English | |
Type of Exam | Written exam |
|
Content | The goal of computer vision is to compute geometric and semantic properties of the three-dimensional world from digital images. Problems in this field include reconstructing the 3D shape of an object, determining how things are moving and recognizing objects or scenes. This course will provide an Course Website: https://uni-tuebingen.de/de/203146 |
|
Objectives | Students gain an understanding of the theoretical and practical concepts of computer vision including image formation, camera models, feature detection, multiple view geometry, 3D |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Geiger | |
Literature / Other | Related literature will be listed throughout the lecture. |
|
Last offered | Sommersemester 2022 | |
Planned for | Sommersemester 2025 | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV |
Module Number ML-4701 |
Module Title An Introduction to Formal Epistemology and Ranking Theory in Particular |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Written Test |
|
Content | inhalt ml 4701 |
|
Objectives | goals ml 4701 |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Spohn | |
Literature / Other | - |
|
Last offered | - | |
Planned for | - | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV |
Module Number ML-4504 |
Module Title Advanced Topics in Data Science and Analytics |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Written Test |
|
Content | inhalt ml 4505 |
|
Objectives | goals ml 4504 |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Kasneci G | |
Literature / Other | - |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS, ML-DIV |
Module Number ML-4430 |
Module Title Machine Learning Approaches in Climate Science |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Written Test |
|
Content | inahlt ml 4431 |
|
Objectives | goals ml 4430 |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Goswami | |
Literature / Other | - |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV |
Module Number ML-4309 |
Module Title Data Compression with and without Deep Probabilistic Models |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | In the summer semester | |
Language of instruction | English | |
Type of Exam | written exam |
|
Content | This course covers lossless and lossy data compression from information theory to applications, and from established compression algorithms to novel machine-learning based methods. Research on data compression has made rapid progress in the last few years. Novel, machine-learning based methods are now beginning to significantly outperform even the best conventional compression methods, in particular for image and video compression. We will discuss and prove information-theoretical foundations of compression (e.g., theoretical bounds on the bitrate, rate/distortion theory, and the source/channel separation theorem). Building on these concepts, we will then first discuss and analyze various established practical algorithms for data compression (e.g., Huffman Coding, Arithmetic Coding, Asymmetric Numeral Systems, and Bits-Back Coding). Finally we will cover the emerging field of machine-learning based data compression, discussing important methods like variational inference and deep probabilistic models such as variational autoencoders. Detailed course schedule: https://robamler.github.io/teaching/compress23/ |
|
Objectives | On the theory side, you will learn information theoretical bounds for lossless and lossy compression, several algorithms for so-called entropy coding with their respective advantages and disadvantages, and the foundations of probabilistic machine learning, in particular scalable approximate Bayesian inference. On the applied side, the tutorials will teach you how to implement entropy coding algorithms in real code and how to train various types of deep probabilistic machine learning models, integrate them into data compression algorithms, and evaluate their performance. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Bamler | |
Literature / Other | I will recommend some relevant literature in the first lecture. However, since this course covers an emerging field of research, there isn't any canonical reference yet that covers all discussed topics. Special-made and recently revised videos for all covered topics as well as lecture notes and solutions to the problem sets will be provided on the course website. Students should already have a sound understanding of multivariate calculus and should be able to write simple programs in Python. Parallel attendance of the course "Probabilistic Machine Learning" will likely be helpful, but is not formally required. |
|
Last offered | Sommersemester 2022 | |
Planned for | Sommersemester 2023 | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV |
Module Number BIOINF4382 (entspricht BIO-4382) |
Module Title Machine Learning for Single Cell Biology |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | In the winter semester | |
Language of instruction | English | |
Type of Exam | Oral Test |
|
Content | Single-cell technologies in conjunction with machine learning approaches are |
|
Objectives | • Overview state-of-the-art single-cell technologies |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Claassen | |
Literature / Other | Programmierkenntnisse Python |
|
Last offered | Wintersemester 2022 | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | BIO-BIO, INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-BIOMED, ML-CS, ML-DIV |
Module Number BIOINF4103 (entspricht BIO-4103) |
Module Title Group Project Bioinformatics |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Written elaboration |
|
Content | The group project serves to deepen the knowledge in a specific area of bioinformatics |
|
Objectives | Bioinformatics group project: The students |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Dozenten/Arbeitsgruppenleiter | |
Literature / Other | Wissenschaftliche Literatur/Veröffentlichungen relevant für das zu bearbeitende Thema |
|
Last offered | Wintersemester 2022 | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | BIO-BIO, MEDZ-BIOMED, MEDZ-SEM |
Module Number BIOINF4104 (entspricht BIO-4104) |
Module Title Seminar Selected Topics in Bioinformatics |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Presentation and written report |
|
Content | In this module we discuss advanced topic in the area of bioinformatics. Information: In the summer term, the seminar "Statistical Population Genomics" (Instructor: Baumdicker) will be offered. |
|
Objectives | Students get to know about advanced topics in bioinformatics theory and applications. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Dozenten der Bioinformatik | |
Literature / Other | Will be handed out in the course |
|
Last offered | unknown | |
Planned for | Sommersemester 2024 | |
Assigned Study Areas | BIO-BIO, BIO-SEM, INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS |
Module Number ML-4506 |
Module Title Machine Learning for Medical Image Analysis |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | In the winter semester | |
Language of instruction | English | |
Type of Exam | Oral presentation and graded participation in paper discussion |
|
Content | The seminar starts with an introductory lecture to provide a compact overview of the research field (machine learning for medical image analysis), as well as a tutorial on critical analysis and presentation of research papers. |
|
Objectives | The learning objectives of this seminar consist of three parts: (1) the students will gain a solid understanding of key contributions to the field of machine learning for medical image analysis, (2) the students learn to critically read and analyse original research papers and judge their impact, and (3) the students will improve their scientific communication skills with an oral presentation and participation in discussions sessions. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Baumgartner, Koch | |
Literature / Other | Will be provided in the course |
|
Last offered | Wintersemester 2022 | |
Planned for | Wintersemester 2023 | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS, ML-DIV |
Module Number ML-4507 |
Module Title Autonomous Vision |
Lecture Type(s) Seminar, Proseminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Report, Review, Presentation, Participation |
|
Content | Under module number ML-4507, the Autonomous Machine Vision Group offers regular proseminars and seminars. Currently, the following proseminars and seminars are offered: Winter semester: Summer semester: |
|
Objectives | Students gain a deep unterstanding of a scientific topic. They learn to efficiently search, navigate and read relevant literature and to summarize a topic clearly in their own words in a written report. Moreover, students present their topic to an audience of students and researchers, and provide feedback to others in the form of reviews and discussions. During the seminar, students learn to put scientific research into context, practice critical thinking and identify advantages and problems of a studied scientific method. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Geiger | |
Literature / Other | - |
|
Last offered | Wintersemester 2022 | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS, ML-DIV |
Module Number INFO-4390 |
Module Title Visual Perception and Learning for Robotics |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Written exam (in case of a small number of participants: oral tests) |
|
Content | - Introduction to robot types and sensors |
|
Objectives | (1) Students can phrase a robotic visual perception problem as algebraic, probabilistic state estimation or machine learning problem and can select an appropriate algorithm for solving it. (2) Students are able to implement a set of robotic visual perception and learning algorithms and analyze their behavior. (3) Students can explain the challenges in robotic visual perception and learning and assess and characterize new methods. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Stückler | |
Literature / Other | Recommended to attend deep learning course before. Basic programming skills in python required. Lecture slides will be provided. Further literature will be announced in the lecture. Recommended textbooks: - An Invitation to 3-D Vision by Yi Ma, Stefano Soatto, Jana Košecká, S. Shankar Sastry - Deep Learning by Goodfellow, Bengio and Courville |
|
Last offered | Wintersemester 2022 | |
Planned for | --- | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, ML-CS, ML-DIV |
Module Number ML-4508 |
Module Title Virtual Humans |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 9 | |
Work load - Contact time - Self study |
Work load:
270 h Class time:
90 h / 6 SWS Self study:
180 h |
|
Duration | 1 Semester | |
Frequency | In the winter semester | |
Language of instruction | English | |
Type of Exam | Oral or Written (depending on the number of students) |
|
Content | A virtual human is a digital representation of a real human. Virtual humans (VH) should look, move and eventually think like real humans. Building such VH is one of the long standing goals of Artificial Intelligence. |
|
Objectives | Understand the mathematical tools and algorithms to build VH from data. At the end of the course, students will be familiar with the state of the art in human motion and shape modeling, estimation of pose, shape and humans in clothing from images and video, as well as learning generative models of human motion conditioned on 3D scene geometry. Students should be able to apply the concepts in practice, develop and train models, reproduce research and conduct original research in this area. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Pons-Moll | |
Literature / Other | Related literature will be posted on the course website (https://virtualhumans.mpi-inf.mpg.de/DH22/). Knowledge of linear algebra, optimization and probability (e.g, mathematics for machine learning) and coding skills (Python). |
|
Last offered | Wintersemester 2022 | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-HCI, MEDI-INFO, MEDI-MEDI, ML-CS, ML-DIV |
Module Number BIOINF4377 (entspricht BIO-4377) |
Module Title From Open Data to Open Science |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | english |
|
Content | english |
|
Objectives | english |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Nahnsen | |
Literature / Other | Originalarbeiten und zusätzliche Literatur wird am Anfang der Vorlesung bekannt gegeben. |
|
Last offered | Wintersemester 2022 | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | BIO-SEM, INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS |
Module Number BIOINF4250 (entspricht BIO-4250) |
Module Title Computational Single Cell Biology |
Lecture Type(s) Practical Course |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | A written report is to be submitted after the course. Performance during the course will also be integrated into the final grade. |
|
Content | The basics of processing and automatically interpreting single-cell datasets are conveyed and applied on concrete examples in this practical course. The course tasks will be implemented in the scripting language Python and R. Specifically this course covers preprocessing, quality control of single-cell transcriptomic data and reconstruction of dynamic processes via RNA velocity analyses, trajectory inference and different dynamic models. |
|
Objectives | (1)The students learn how to perform quality control and normalization of single-cell RNA sequencing data. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Claassen | |
Literature / Other | Will be supplied during the course. |
|
Last offered | Wintersemester 2022 | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | BIO-PRAK |
Module Number INFO-4501 |
Module Title Digitalization & Innovation |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | In the summer semester | |
Language of instruction | English | |
Type of Exam | Written exam |
|
Content | In this course, students are provided with a broad overview on existing up-to-date theories (e.g. disruptive innovation), tools (e.g. data analytics, blockchain), methods (e.g. product management, agile project management) and technologies (e.g. 3D printing, robotics) in the context of digitization, digitalization and digital transformation along the entire value chain in industry. Focusing from strategy to implementation, the course is aimed to provide a diverse knowledge empowered by various practical examples, e.g. in case studies. |
|
Objectives | The students are able to explain the content presented and to analyze and evaluate the suitability of digitization technologies in industry. They can assess the applicability of methods such as disruptive innovation and agile project management. They know how to classify and evaluate the practical challenges of digitization in industry. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Wahl | |
Literature / Other | - |
|
Last offered | unknown | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, ML-CS |
Module Number ML-4440 |
Module Title Trustworthy Machine Learning |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Written exam (oral exam in case of a small number of participants) |
|
Content | As machine learning technology gets applied to actual products and solutions, new challenges have emerged. Models unexpectedly fail to generalise well to small changes in the distribution; some models are found to utilise sensitive features that could treat certain demographic user groups unfairly; models tend to be confident on novel types of data; models cannot communicate the rationale behind their decisions effectively with the end users like medical staff to maximise the human-machine synergies. Collectively, we face a trustworthiness issue with the current machine learning technology. A large fraction of the machine learning research nowadays is dedicated to expanding the frontier of Trustworthy Machine Learning (TML). The course covers a theoretical and technical background for key topics in TML. We conduct a critical review of important classical and contemporary research papers on related topics and provide hands-on practicals to implement TML techniques. |
|
Objectives | Students will be able to critically read, assess, and discuss research work in Trustworthy Machine Learning (TML). They will gain the technical background to implement basic TML techniques in a deep learning framework. They will be ready to conduct their own research in TML and make contributions to the research community. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Oh | |
Literature / Other | Prerequisites: Students should be familiar with Python and PyTorch coding. They should have basic knowledge of machine learning concepts: supervised learning, function fitting, generalisation gap, overfitting, and regularisation. Furthermore they should have basic knowledge of deep learning (CNNs and Transformers, their components, and empirical techniques for training and evaluating them), for instance by having passed ML-4103 (or equivalent). Basic maths skills (multivariate calculus, linear algebra, probability, statistics, and optimisation) are also required. |
|
Last offered | Wintersemester 2022 | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV |
Module Number MEDZ-4710 |
Module Title Data Privacy |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Oral presentation and written report |
|
Content | This seminar covers current research topics in the field of data privacy and their applications. Topics include (but are not limited to) social network privacy, machine learning privacy, and biomedical data privacy. |
|
Objectives | Students will learn, summarize, and present state-of-the-art scientific papers in data privacy. They can critically assess the contributions of a paper. They can present current research results to other students and researchers, and can lead research discussions. They can summarize and evaluate the results of research papers in form of a oral presentation and a written report. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Akgün | |
Literature / Other | Wird in der Vorbesprechung bekannt gegeben / Will be announced in a preparatory meeting at the start of the semester |
|
Last offered | Wintersemester 2022 | |
Planned for | currently not planned | |
Assigned Study Areas | BIO-SEM, INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS |
Module Number BIOINF4384 (entspricht BIO-4384) |
Module Title Machine Learning of Single-Cell Dynamics |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | In the summer semester | |
Language of instruction | English | |
Type of Exam | Excercises (not graded) must be passed. Written / oral exam (graded). |
|
Content | Single-cell technologies have been used to reconstruct the dynamics of biological processes, such as signaling, differentiation and development. This course will review different types of technologies that have been developed and used to this end. At the core, this lecture will introduce and discuss different mathematical models for cellular dynamics, as well as classical and machine learning based system identification and model selection approaches to learn such models from single-cell data. |
|
Objectives | (1) Overview of time resolved single-cell technologies |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Claassen | |
Literature / Other | Requirements: Programming skills in Python. |
|
Last offered | unknown | |
Planned for | Sommersemester 2024 | |
Assigned Study Areas | BIO-BIO, ML-CS, ML-DIV |
Module Number BIOINF4383 (entspricht BIO-4383) |
Module Title Advanced Topics in Machine Learning for Single Cell Biology |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | In the summer semester | |
Language of instruction | English | |
Type of Exam | Presentation (about 30 minutes) and written elaboration (approx. 10 pages), leading the discussion once |
|
Content | This seminar builds on the lecture 'Machine Learning for Single Cell Biology' (BIO-4382) and discusses current scientific publications on machine learning method development and application for basic science and translational single-cell biology studies. In the summer term 2024, the seminar "Machine Learning in Translational Single-Cell Biology" will be offered. |
|
Objectives | (1) Reading and comprehension of state-of-the-art publications in the field Machine Learning for Single Cell Biology |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Claassen | |
Literature / Other | Teilnahmevoraussetzungen: BIO-4382 oder vergleichbare Veranstaltung / |
|
Last offered | unknown | |
Planned for | Sommersemester 2024 | |
Assigned Study Areas | BIO-BIO, BIO-SEM, INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS, ML-DIV |
Module Number BIOINF4366 (entspricht BIO-4366) |
Module Title Data Visualization in Biology and Medicine |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Will be announced at the beginning of the semester. |
|
Content | Visualization plays an important role for data analysis as well as for the communication of findings in biology and medicine. As the data in these fields is complex and diverse---ranging from abstract data like gene expression, biological networks, or electronic health records to spatial data like molecular structures or medical imaging data---a wide range of different visualization methods have been developed. In the last decade, however, advances in visualization not only focus on creating meaningful representations of these data, but also on the development of novel visual analytics applications, which combine visualization with data analysis methods (e.g., by applying methods from machine learning for feature extraction). This "computer-assisted human-in-the-loop'' approach provides more comprehensive information and allows users to interactively explore their data. In this seminar, we will discuss seminal methods and recent advances in the field of data visualization for biology and medicine. Special focus will be on interactive visualization and visual analytics techniques and how methods from one field can be applied in the other one. |
|
Objectives | Students will know current visualization methods for biological and medical data. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Krone | |
Literature / Other | Course prerequisites: No formal requirements, but background knowledge in scientific/information visualization, computer graphics, or data science is helpful. Literature: Will be announced at the beginning of the semester. |
|
Last offered | unknown | |
Planned for | Sommersemester 2023 | |
Assigned Study Areas | BIO-BIO, BIO-SEM, INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS |
Module Number INFO-4504-V |
Module Title Understanding Vision - Lecture |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | In the summer semester | |
Language of instruction | English | |
Type of Exam | Written exam |
|
Content | - |
|
Objectives | - |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Li | |
Literature / Other | Literatur / Literature: Lehrbuch / Textbook "Understanding vision"; Voraussetzungen / Prerequisites: |
|
Last offered | unknown | |
Planned for | Sommersemester 2024 | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, ML-CS |
Module Number INFO-4504-S |
Module Title Understanding Vision - Seminar |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | In the summer semester | |
Language of instruction | English | |
Type of Exam | Oral presentation, term paper |
|
Content | - |
|
Objectives | - |
|
Prerequisite for participation | INFO-4504-V Understanding Vision - Lecture | |
Lecturer | Li | |
Literature / Other | Literatur / Literature: Voraussetzungen / Prerequisites: |
|
Last offered | unknown | |
Planned for | Sommersemester 2024 | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS |
Module Number INFO-4165 |
Module Title Foundation of Signals and Linear Systems |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German | |
Type of Exam | - |
|
Content | Topics are: Fourier-transformation, Laplace-transformation, z-transformation, linear systems and differential equations, impulse response, step function response, transfer function. |
|
Objectives | The students can describe the properties of time-continuous and time-discrete signals and of linear time-invariant systems in the time and frequency domain and can apply the concepts of Fourier, Laplace and z-transformation to analyze and solve problems in the areas of signal processing and control. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Schilling | |
Literature / Other | Eigene Materialien und Vorlesungsfolien werden zum Download bereitgestellt. / Own materials and lecture slides will be available for download. |
|
Last offered | unknown | |
Planned for | Sommersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, MEDI-VIS, ML-CS |
Module Number INFO-4451 |
Module Title Introduction to Cryptography |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Each semester | |
Language of instruction | English | |
Type of Exam | TBD |
|
Content | Cryptography is today an essential part of the security of all modern communications, secure data storage, and confidential computing. This course will provide an introduction to all of the most fundamental principles, methods, and definitions in the field of cryptography, in addition to a review of some of the most important applications. Topics are: • classical cryptographic systems, |
|
Objectives | TBD |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Akgün | |
Literature / Other | -- |
|
Last offered | Sommersemester 2022 | |
Planned for | Sommersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-WEB, MEDZ-MEDTECH, ML-CS |
Module Number BIOINF-4510 (bisher: BIO-4510) |
Module Title Applied Statistics for Biomedical Data Analysis |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Presentation (about 30 minutes) and written elaboration (approx. 10 pages), leading the discussion once |
|
Content | In this seminar, current topics of applied statistics for bioinformatics data analysis will be discussed. Furthermore, we will also discuss classical concepts of stochastics/statistics and discrete mathematics. These will include, among others, the following: introduction to randomness, elementary and advanced combinatorics, random variables, discrete probability distributions and where they come from, conditional probabilities, Bayes? Theorem, continuous probability distributions, posterior distributions, descriptive statistics, moments of random variables (expectation, variance, ?), parametric models, statistical testing (frequentist view), statistical testing (Bayesian view), parameter estimation: maximum likelihood, parameter estimation in mixture models: EM algorithm, regression (simple linear, logistic, robust, multiple), robust regression, multiple regression, logistic regression. All concepts will be discussed in close relation to to current research in biomedice. |
|
Objectives | The students can independently work with supervision on a challenging topic. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Nahnsen | |
Literature / Other | Bücher und Forschungsartikel / Books and research articles |
|
Last offered | unknown | |
Planned for | Sommersemester 2023 | |
Assigned Study Areas | BIO-BIO, BIO-SEM, INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS |
Module Number MEDZ-4523 |
Module Title Machine Learning to Fight Infections |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Oral presentation and written report |
|
Content | Machine learning is used in many areas of medicine to automate certain processes, find intelligent representations of complex data, and make predictions about phenotypes of interest or other labels. Machine learning techniques have also been applied and developed in infection research for quite some time. In this seminar, we will cover several areas ranging from Machine Learning assisted Computational Epidemiology, to Resistance Prediction of Infectious Agents, to Predicting Viral Evolution. |
|
Objectives | The students know and can critically reflect the most important concepts, theories and methods in how to control infections with machine learning metho |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Pfeifer | |
Literature / Other | - |
|
Last offered | unknown | |
Planned for | Sommersemester 2024 | |
Assigned Study Areas | BIO-BIO, BIO-SEM, INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM, ML-CS |
Module Number INFO-4193 |
Module Title Natural Language Processing |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | In the summer semester | |
Language of instruction | English | |
Type of Exam | Oral examination (written exam if there are a large number of participants), exercise points can be included as a grade bonus in the assessment of the exam |
|
Content | Natural Language Processing (NLP) is a sub-field of artificial intelligence that aims at understanding and automatic generation of texts for various applications, such as document classification, sentiment analysis, text summarization, speech recognition, etc. This course covers NLP topics including n-gram models, word embeddings, bag of word representations for document classification, classifiers, tokenization, part of speech tagging, matrix factorization and topic modeling, deep learning for language processing, transformers, language models and text generation, and finally applications such as document summarization, machine translation, or question answering. |
|
Objectives | Course participants will learn from basic to advanced topics in NLP. They will learn to analyze datasets of textual documents and uncover their various patterns, build text classification models, text generation models and a few modern applications of NLP. The course exercises will give students an opportunity to solve real-world NLP problems independently. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Eickhoff | |
Literature / Other | Verwendete Programmiersprache: Python |
|
Last offered | unknown | |
Planned for | Sommersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-HCI, MEDI-INFO, MEDI-MEDI, MEDI-MMT, ML-CS, ML-DIV |
Module Number INFO-4271 |
Module Title Modern Search Engines |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | In the summer semester | |
Language of instruction | English | |
Type of Exam | Final Project Presentation and Report |
|
Content | Search engines are the main interface between people and humankind's massive globally distributed repositories of knowledge. In this practice-focused course, we will review information retrieval basics such as web crawling, content indexing, index compression, query processing, and result ranking, before moving on to advanced techniques for personalization, (dense) neural retrieval, and stochastic ranking. The capstone to this class will be a practical project in which students design and build their own search engines that will be evaluated in a retrieval competition. |
|
Objectives | In this project-oriented practical course, students learn how to design and implement modern search engines. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Eickhoff | |
Literature / Other | Verwendete Programmiersprache: Python. |
|
Last offered | unknown | |
Planned for | Sommersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-WEB, ML-CS |
Module Number ML-4511 |
Module Title Machine Learning in Gaphics, Vision, and Language |
Lecture Type(s) Practical Course |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | In the summer semester | |
Language of instruction | English | |
Type of Exam | Project, presentation, and written elaboration |
|
Content | Implementation of advanced applications and programs in the intersection of machine learning in computer graphics / computer vision / natural language processing |
|
Objectives | Students will know how to efficiently implement current machine learning approaches in the areas of segmentation, 3D reconstruction, scene analysis, rendering, interaction, or language processing. They will be able to independently plan and execute programming projects in groups using neural networks, transformers or other ML approaches for data acquisition, reconstruction and representation as well as for natural language interaction or explanation. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Lensch | |
Literature / Other | Teilnahmevoraussetzungen: Deep Learning, von Vorteil sind Graphische Datenverarbeitung oder Computer Vision ---- Course prerequisites: Completion of Deep Learning; previous completion of Computer Graphics or Computer Vision is advantageous |
|
Last offered | unknown | |
Planned for | Sommersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-PRAX, MEDI-VIS, ML-CS, ML-DIV |
Module Number BIOINF4260 (entspricht BIO-4260) |
Module Title Bioinformatics Methods and Visual Analytics of Biological Data |
Lecture Type(s) Practical Course |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | The final grade is based on collaboration and performance,a lab journal of week 1, a written report on the project of week 2 of the practical course, and one or two short oral presentations. |
|
Content | The focus of this practical course is placed on the design and practical implementation of effective visualizations of biological data. Students will learn guidelines and use of methods for visualizing data. This hands-on course uses real-world data; the focus is on the entire process of designing and implementing effective visualization and visual analysis of biological data, from data and task analysis to selecting appropriate visualization methods and designing interactive visual analytics applications; different methods are compared in terms of their effectiveness for analysis and communication. Topics of the first weekinclude color and perception, bioinformatics methods for data preprocessing, visualization techniques for biological data, for example multidimensional and temporal data, networks and structures, and the basics of visual analytics and visual storytelling. During the second week students work on a self-chosen small research project that combines bioinformatics with visualisation methods. |
|
Objectives | Students gain practical experience in designing and programming interactive visualizations for the analysis of biological data. They are able to use libraries and frameworks and acquire or expand their knowledge of JavaScript (mainly D3) and Python. By working in groups, students acquire teamwork and collaboration skills and learn project organization and presentation techniques. Students know the strengths and weaknesses as well as the limitations of different visualization methods and can describe and evaluate these methods. |
|
Prerequisite for participation |
BIOINF4110 (entspricht BIO-4110) Sequence Bioinformatics, BIOINF4120 (enstpricht BIO-4120) Bioinformatics of Structures and Systems, BIOINF4364 (entspricht BIO-4364) Visualization of Biological Data |
|
Lecturer | Krone, Nieselt | |
Literature / Other | Will be provided at the beginning of the course, if necessary. |
|
Last offered | unknown | |
Planned for | Sommersemester 2023 | |
Assigned Study Areas | BIO-PRAK, MEDZ-BIOMED |
Module Number INFO-4368 |
Module Title Current Topics in Robotics |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Oral presentation and written report (term paper) |
|
Content | This seminar covers varying current advanced topics in robotics with a special focus on mobile robotics. Topics are, for instance, robot kinematics, modern probabilistic methods of navigation and self-localization, mapping, path planning with moving obstacles, robot formations, simultaneous localization and mapping (SLAM), visual self-localization, and sensor fusion with different sensors. In contrast to the proseminar offered for bachelor students in this thematica area, the topics, algorithms and math/physics descriptions in this master seminar are more demanding, and the treatment is more in-depth. |
|
Objectives | Students are able to scientifically analyse a topic from the field of robotics (with a special focus on mobile robots), and they can present it and elaborate on it in a paper. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Zell | |
Literature / Other | - |
|
Last offered | unknown | |
Planned for | Wintersemester 2023 | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, MEDZ-SEM, ML-CS, ML-DIV |
Module Number MEDI-4513 |
Module Title Audiovisual Media II (Advanced 3D-Animation) |
Lecture Type(s) Practical Course |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | In the summer semester | |
Language of instruction | German | |
Type of Exam | Workpiece with documentation |
|
Content | As part of this course, students expand their knowledge of 3D animation, based on the course "Audiovisual Media II (3D Animation)". In this context, they learn the techniques of organic modeling and create a simple character, which they then texture and put into perspective. A short animated film concludes the course. The course not only teaches a lighting concept, but also promotes cinematic thinking to provide students with a solid foundation for professional work as computer animators. |
|
Objectives | Students have in-depth knowledge of advanced 3D animation techniques and have gained an understanding of film design. Through a short project, they have acquired hands-on knowledge that they can apply as computer animators. |
|
Prerequisite for participation | MEDI-4511 Audiovisual Media II (3D-Animation) | |
Lecturer | Schilling | |
Literature / Other | Literatur: Richard Williams "Animator's Survival Kit" Teilnahmevoraussetzung: Erfolgreiche Teilnahme an MEDI-4511 / MEINF-4511 "Audiovisuelle Medien II (3D-Animation)" |
|
Last offered | unknown | |
Planned for | Wintersemester 2023 | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, MEDI-PRAX, ML-CS |
Module Number INFO-4445 |
Module Title Algebraic Structure and Complexity of Formal Languages |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German | |
Type of Exam | oral exam |
|
Content | Many constructions and procedures of regular languages are only possible because of the finiteness of the underlying algebraic structures. However, the construction principles are also valid at infinity and are feasible for selected non-regular case studies in the lower complexity range. The thorough treatment of the examples with the participation of the students gives the lecture an exercise character in places. |
|
Objectives | An understanding of the basic algebraic constructions of formal languages, and an overview of the relationships between formal languages and complexity |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Lange | |
Literature / Other | Literatur / Literature: -- Voraussetzungen / Prerequisites: Grundlagen der regulaeren und kontextfreien |
|
Last offered | unknown | |
Planned for | Wintersemester 2023 | |
Assigned Study Areas | INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS |
Module Number INFO-4148 |
Module Title Machine Learning in Database Systems and Data Management |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German | |
Type of Exam | Written exam |
|
Content | - |
|
Objectives | - |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | ||
Literature / Other | - |
|
Last offered | unknown | |
Planned for | Wintersemester 2023 | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, ML-CS |
Module Number BIOINF-4399 (bisher: BIO-4399) |
Module Title Advanced Topics in Bioinformatics |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Written or oral exam |
|
Content | In this course, we explore new and important developments in bioinformatics. This can be driven by new technologies, new biological questions with a computational angle, or new methodologies. Typically, in the first third of the course we will cover the background and introductory material. We then study the new developments in detail in the second third of the course. In the final third, we discuss open problems and possible solutions. |
|
Objectives | The aim of this course is to expose students to current research areas in bioinformatics. |
|
Prerequisite for participation | BIOINF4110 (entspricht BIO-4110) Sequence Bioinformatics | |
Lecturer | Dozenten der Bioinformatik | |
Literature / Other | Literatur: Skripte und Originalartikel Literature: Detailed script, original articles |
|
Last offered | unknown | |
Planned for | currently not planned | |
Assigned Study Areas | BIO-BIO, MEDZ-BIOMED, MEDZ-RES |
Module Number INFO-4367 |
Module Title Topics in Robot Vision |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Oral presentation |
|
Content | In this seminar we will review scientific literature on classical and modern approaches to robot vision with a focus on event cameras. |
|
Objectives | Students are able to scientifically analyse a topic from the field of robot vision, and they can present it and elaborate on it in a paper. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Zell | |
Literature / Other | - |
|
Last offered | unknown | |
Planned for | Sommersemester 2024 | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-MMT, MEDZ-SEM, ML-CS, ML-DIV |
Module Number BIOINF4270 |
Module Title Computational Workflows for Biomedical Data |
Lecture Type(s) Practical Course |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Presentation and defense of the research results (oral and written) |
|
Content | In this practical course, students will learn to model complex biomedical data analysis task using workflows. Using abstract workflow descriptions, they will implement new computational workflows using the workflow language nextflow. Students will also re-use existing workflows and provide scientific solutions to the biomedical data science problem at hand. |
|
Objectives | After completing this practical course students can asbtract problems in biomedical research and models them as workflow. They are able to implement simple workflow with "nextflow" and to use various existing "best-practice" workflows. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Nahnsen | |
Literature / Other | Ewels et al., Nature Biotechnology, 2020; Weitere Literatur wird im Praktikum bekannt gegeben / Further literature will be announced in the course. |
|
Last offered | unknown | |
Planned for | Sommersemester 2024 | |
Assigned Study Areas | BIO-PRAK |
Module Number INFO-4355 |
Module Title IT Security (Seminar) |
Lecture Type(s) Seminar |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German and English | |
Type of Exam | depending on the seminar |
|
Content | TBA |
|
Objectives | TBA |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Huber, Menth | |
Literature / Other | - |
|
Last offered | unknown | |
Planned for | Sommersemester 2024 | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, MEDZ-SEM |
Module Number INFO-4356 |
Module Title Network Security II (6 ECTS) |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German and English | |
Type of Exam | Written exam (oral exam if the number of participants is small), points gained in the exercises may be transferable as bonus points into the exam. |
|
Content | The lecture covers the following topics: Layer-2 Security, Perimeter Security, Anonymization, Blockchain, Advanced Topics; the lecture is accompanied by an extensive practice session that illustrates and deepens the acquired knowledge with practical examples. |
|
Objectives | Network Security II: Students have a comprehensive and in-depth understanding of network security. They are able to apply their acquired problem-solving skills also in new and unfamiliar contexts. They are able to acquire new knowledge and skills independently and to exchange information, ideas, problems and solutions with experts on a scientific level. |
|
Prerequisite for participation | INFO-4341 Network Security I | |
Lecturer | Menth | |
Literature / Other | - |
|
Last offered | unknown | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-TECH, MEDI-APPL, MEDI-INFO, MEDI-MEDI, MEDI-WEB, ML-CS |
Module Number ML-4311 |
Module Title Nonconvex Optimization for Deep Learning |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Written exam (in case of a small number of participants: oral tests) |
|
Content | Website: https://institute-tue.ellis.eu/en/lecture-deep-optimization Note: This lecture does not overlap with "Convex and Nonconvex Optimization." While students are encouraged to take "Convex and Nonconvex Optimization" to solidify their understanding of SGD and basic optimization concepts (duality, interior point methods, constraints), we will only discuss optimization in the context of training deep neural networks and often drift into discussions regarding model design and initialization. Successful training of deep learning models requires non-trivial optimization techniques. This course gives a formal introduction to the field of nonconvex optimization by discussing training of large deep models. We will start with a recap of essential optimization concepts and then proceed to convergence analysis of SGD in the general nonconvex smooth setting. Here, we will explain why a standard nonconvex optimization analysis cannot fully explain the training of neural networks. After discussing the properties of stationary points (e.g., saddle points and local minima), we will study the geometry of neural network landscapes; in particular, we will discuss the existence of "bad" local minima. Next, to gain some insight into the training dynamics of SGD in deep networks, we will explore specific and insightful nonconvex toy problems, such as deep chains and matrix factorization/decomposition/sensing. These are to be considered warm-ups (primitives) for deep learning problems. We will then examine training of standard deep neural networks and discuss the impact of initialization and (over)parametrization on optimization speed and generalization. We will also touch on the benefits of normalization and skip connections. Finally, we will analyze adaptive methods like Adam and discuss their theoretical guarantees and performance on language models. If time permits, we will touch on advanced topics such as label noise, sharpness-aware minimization, neural tangent kernel (NTK), and maximal update parametrization (muP). Prerequisites: |
|
Objectives | The objective is to provide the student with an understanding of modern neural network training pipelines. After the lecture, they will have known both the theoretical foundations of non-convex optimization and the main ideas behind the successful training of deep learning models. |
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Orvieto | |
Literature / Other | Here are a few crucial papers discussed in the lecture (math will be greatly simplified): |
|
Last offered | unknown | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | INFO-INFO, INFO-THEO, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV |
Module Number ML-4331 |
Module Title The Science of Machine Learning Benchmarks |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | Written exam |
|
Content | Benchmarks have played a central role in the progress of machine learning research since the 1980s. Although there's much researchers have done with them, we still know little about how and why benchmarks work. This class covers the emerging science of benchmarks. The first part focuses on laying the theoretical and empirical foundations that we build on throughout the class. The second part covers lessons about reliability and validity we draw from influential benchmarks, such as ImageNet. The final part turns to benchmarking and evaluation in the era of large language models. Students who would like to attend this course should meet the following requirements: |
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Objectives | Working from first principles, the aim is to better understand why and when benchmarks work, how they fail, and how to best evaluate machine learning models. At the end of the class, students have a good understanding of machine learning benchmarks and the surrounding evaluation ecosystem. They can follow best practices in the evaluation of machine learning. They are able to identify and avoid pitfalls. |
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Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Hardt, MPI | |
Literature / Other | - |
|
Last offered | unknown | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV |
Module Number INFO-4148 |
Module Title Machine Learning in Database Systems and Data Management |
Lecture Type(s) Lecture |
---|---|---|
ECTS | 3 | |
Work load - Contact time - Self study |
Work load:
90 h Class time:
30 h / 2 SWS Self study:
60 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | German | |
Type of Exam | Written exam |
|
Content | The lecture contains the following topical blocks. - Key concepts of relational Database management systems (DBMS) |
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Objectives | After completing this lecture, students understand how machine learning methods can be used in the context of database systems and data management. They are able to apply machine learning methods to concrete problems when using database systems. |
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Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Hechler | |
Literature / Other | - |
|
Last offered | unknown | |
Planned for | Wintersemester 2023 | |
Assigned Study Areas | INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, ML-CS |
Module Number ML-4702 |
Module Title Being a Scientist: Making Meaning by Making Science |
Lecture Type(s) Lecture, Tutorial |
---|---|---|
ECTS | 6 | |
Work load - Contact time - Self study |
Work load:
180 h Class time:
60 h / 4 SWS Self study:
120 h |
|
Duration | 1 Semester | |
Frequency | Irregular | |
Language of instruction | English | |
Type of Exam | 1. Project throughout the semester to identify one’s research quest. |
|
Content | This course focusses on the subjective experience of being a scientist. It is not a mechanical how-to. We do not talk about how to do statistics or conduct experiments or write papers or grant proposals. Rather we discuss and provide insights and tools to help students / beginning scientists live a more meaningful life in doing science from their own subjective experience. To that end, the course will be organised around three broad headings: 1. Ways of looking at Science (What are the different ways of looking at science, and how does that affect how one feels about it and approaches it? As well as the traditional ways (science as knowledge or science as institution); a particular focus will be on science as personal – the view of science from the subjective experience of the working scientist). 2. Ways of doing Science (with questions like, e.g., How do you choose a good question? What happens when you get stuck? What are the cognitive tools that help you do better science? How does your attitude to science affect what you do? How do you cope with failure and getting stuck? How do you navigate and thrive in the social side of science?) 3. Ways of making meaning (i.e., The personal challenges of being a scientist, and what one can do about them. How to deal with the inevitable crap? How to raise yourself above the menial and crappy side of things to attain some transcendence and create meaning for yourself by doing science?) The tutorials will be organised around a series of questions which will be posed in advance. Students should come prepared to discuss the questions. |
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Objectives | At the end of the course, it is expected / desired that students will: |
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Prerequisite for participation | There are no specific prerequisites. | |
Lecturer | Williamson | |
Literature / Other | will be provided by the lecturer |
|
Last offered | unknown | |
Planned for | Wintersemester 2024 | |
Assigned Study Areas | INFO-INFO, MEDI-APPL, MEDI-INFO, ML-CS |