This 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:
- Video Deblurring
- Semantic Segmentation
- Stereo & Multi-view Reconstruction
- Optical Flow
- Rendering Faces
- Global Illumination Sampling
- Course number: ML-4330
- Credits: 6 ECTS (2h lecture + 2h exercise)
- Total Workload: 180h
- Basic math and coding skills (in particular Python and PyTorch)
- Basic knowledge about deep neural nets is beneficial
- Given the current COVID-19 situation, all lectures and exercises will be held virtually until further notice. We will provide all lectures and introduction to the exercises on Tuesday or Wednesday evening as video recordings through OneDrive (linked in ILIAS). You can either watch the videos directly in your browser (low resolution) or download them locally to watch them using VLC (FullHD). The first videos will be uploaded on April 21/22. The exercise assignments will be handed out and graded through ILIAS.
- On Friday we will host a lecture Q&A session from 9:30 to 10:00 and an exercise Q&A session from 10:00 to 11:00 via zoom. The first lecture and exercise Q&A session will take place on April 24. The Q&A sessions will take place via Zoom (linked in ILIAS). Please make sure you have the latest Zoom client installed.
- Students shall watch both the lecture and exercise videos before the Q&A session on Friday and take note of questions that they like to have answered during the Q&A sessions. Students shall enter their questions into the editable OneDrive Questions document linked in ILIAS (preferred) or ask them during the Q&A session. Every student should prepare at least one question. Additional questions or technical questions regarding the exercises can also be posted on the ILIAS forum.
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 will require coding in Python and PyTorch. Make sure you are familiar with Python. Prior experience with PyTorch or Tensorflow is not required but a plus.