- Lesson 1: Introduction
- Lesson 1: Robust Fitting
- Lesson 2: Global Optimization I
- Lesson 3: Global Optimization II
- Lesson 4: Representation and Approximation of Visual Data
- Lesson 5: Dimension Reduction
- Lesson 6: Sampling Patterns
- Lesson 7: Markov Random Fields and Graph-cuts
- Lesson 8: Space Transformations
- Lesson 9: Variational Methods I
- Lesson 10: Variational Methods II
- Lesson 11: Deep Learning
- Lesson 12: Variational Methods III
- Lesson 13: Structure Learning