Machine Learning in Graphics & Vision (ML-4330)

Content

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

Overview

  • SWS: 2 V + 2 Ü
  • 6 ECTS

Lectures
Thursdays 8–10, Lecture Hall Maria-von-Lindenstraße 6, first lecture on 16. April 2020

Exercise groups
Fridays 8-10, Lecture Hall Maria-von-Lindenstraße 6, first exercise meeting on 18. April 2020

 

News

Please enroll in ILIAS

Exercises

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.