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 |
Workload:
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. |
|
Allocation of credits / grading |
Type of Class
Status
SWS
Credits
Type of Exam
Exam duration
Evaluation
Calculation
of Module (%)
Lecture
V
o
2
3.0
wt
90
g
100
Tutorial
Ü
o
2
3.0
|
|
Prerequisite for participation | There are no specific prerequisites. | |
Lecturer / Other | Geiger, Zell | |
Literature | 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 |