Module Number

INF3657 		Module Title

Machine Learning (Proseminar) 		Type of Module

Elective Compulsory
ECTS 3
Work load
- Contact time
- Self study
Workload:
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

Presentation and essay
Lecture type(s) Proseminar
Content

Machine learning methods play an important role in data analysis and modeling in both industry and research. These methods can learn models from data and apply them to unknown instances. Examples of practical applications include character recognition, image recognition, shopping cart analysis, spam filtering or property prediction of chemical compounds. Basic machine learning techniques, their theoretical foundations and their practical applications are presented. In addition, validation strategies and parameter optimization methods are presented.
Objectives

In addition to the subject-specific skills of the proseminar, students also learn how to scientifically analyse a topic, prepare a scientific presentation, give a presentation, communicate with an audience, engage in critical scientific discourse and write a scientific paper on their seminar topic.
Allocation of credits / grading
Type of Class
Status
SWS
Credits
Type of Exam
Exam duration
Evaluation
Calculation
of Module (%)
Prerequisite for participation There are no specific prerequisites.
Lecturer / Other
Literature

Literatur wird in der Vorbesprechung angegeben.
Last offered unknown
Planned for currently not planned
Assigned Study Areas INFM1510, MEINFM1510