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:

• Classification
• Regression
• 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

Lectures
Thursdays 8–10, Room F122 first lecture on 19. April 2018

Exercise groups
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.