Lecturer: Vaclav Smidl
Lesson 1: Review of probability and trivial models
Lesson 2: Prior distributions
Lesson 3: Approximate Inference
Lesson 4: Variational Bayes + Least Squares
Lesson 5:Models with sparse parameters
Lesson 6: Mixture models
Lesson 7: Modeling Challenge Patlak plot
Lesson 8: Blind Source Separation
Lesson 9: Bayesian filtering
Lesson 10: Monte Carlo
Lesson 11: Bayesian Neural networks
Lesson 12: Variational Autoencoders