Lecture 1 - ML, MAP, K Nearest Neighbors.
Lecture 2 - Gaussian Mixture Models.
Lecture 3 - Linear and logistic regression.
Lecture 4 - Neural networks: Back Propagation.
Lecture 5 - Overfitting, Regulazation and Dropout.
Lecture 6 - Decision Trees & MDL.
Lecture 7 - Tree Distribution.
Lecture 8 - Sequential ML. Recurrent Neural Networks (RNN), Long Short Time Memory (LSTM).
Lecture 9 - MINE. Mutual Information Neural Estimation (MINE) and hypothesis testing.
Lecture 10 - f Divergence. f-Divergence and Data Processing Inequality.
Lecture 11 - Variational Inference.
Lecture 12 - Variational Autoencoders: Version 1, Version 2.
Appendix 1- Information measures [pdf].
Information Theory course - recommended.
Reinforcement Learning Course - recommended after undertanding the fundementals of this course.
Python tutorial.