Lecture: Machine Learning in Graphics & Vision


This 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:

  • Video Deblurring
  • Semantic Segmentation
  • Stereo & Multi-view Reconstruction
  • Optical Flow
  • Rendering Faces
  • Global Illumination Sampling




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 Tensorflow. Make sure you are familiar with Python. Prior experience with PyTorch or Tensorflow is not required but a plus.