Vorlesung: Machine Learning in Graphics & Vision
This new course will cover machine learning algorithms as well as their application to computer vision and computer graphics problems.
Machine Learning Topics:
- Random Forests
- Deep Neural Networks
- Generative Models
- Generative Adversarial Networks
- Structured Prediction
- MRF / CRF
Vision and Graphics Applications:
- Semantic Segmentation
- Optical Flow
- Structure from Motion
- Video Deblurring
- Rendering Faces
- Global Illumination Sampling
- SWS: 2 V + 2 Ü
- 6 ECTS
- Veranstaltungsnummer: INF
Thursdays 8–10, Room F122 first lecture on 19. April 2018
Fridays 8-10, Room F122 first exercise meeting on 20. April 2018
- Please enroll in ILIAS
By continuous and active participation in the weekly exercises, students may obtain a 0.3 bonus on the final grade, when passing the exam. To qualify for this bonus, the student must successfully solve 60% of the assigned homework problems which will be determined by grading the submitted homework solutions.
Homework problems might require coding in Python or C++. Make sure you are familiar with Python. If you have a lot of programming experience but in a different language, you will probably be fine.
To be able to login into our machines in the computer pool, you are required to fill out the application for a WSI user account.