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Deep Learning Course, Computer Vision Section

Prof. Thomas Brox

Deep learning has brought a revolution to AI research. A good understanding of the principles of deep networks and experience in training them has become one of the main assets for successful research and development of new technology in machine learning, computer vision, and robotics.

In this course, which will be jointly organized by the machine learning group, the neuorobotics group, the autonomous intelligent systems group, and the computer vision group, we want to teach students the practical knowledge that is needed to do research with deep learning in any of these fields. The course starts with some introductory lectures, continues with first some smaller and then larger projects. You must work in teams of 2-3 persons. There will be a final presentation of your project results at the end of the semester.

Lecture/Exercises:
(2 SWS)
Monday, 14-16
Room: Computer Pool 00-029, Building 082
Contact persons: Artemij Amiranashvili, Mohammadreza Zolfaghari

Beginning: Monday, October 16, 2017

ECTS Credits: 4 or 6

Requirements: Fundamental programming skills in C/C++. Some experience with the Linux toolchain (text editor, compiler, linker, debugger) is recommended.

Remarks: The short lectures will be in English.

The course is open to both Bachelor students (for their Bachelor project) and Master students (for their lab course). To reflect the larger experience of Master students, they must finish more involved project tasks to pass. The final project also provides 4 and 6 ECTS variants of the Bachelor project.

Final presentations: tba.

Slides:

20.11 Segmentation
11.12 Hyperparameter
15.01 GANs and CV projects
15.01 ML projects


Exercise material:

20.11 Autoencoder exercise
27.11 Segmentation exercise
11.12 Hyperparameter exercise
18.12 Hyperparameter 2 exercise