The Yandex Machine Learning School was organised from the 16th to 26th January. It was a quite intensive school with a series of lectures, homework, assignments, tutorials and exam over the course of 10 days only. In particular, topics covered included the following:

  1. Introduction to Machine Learning
  2. $k$-Nearest Neighbour
  3. Model Complexity
  4. Principle Component Analysis, PCA
  5. Singular Value Decomposition
  6. Linear Regression
  7. Linear Classification
  8. Kernel Trick
  9. Classifier Evaluation
  10. Decision Trees
  11. Ensemble Methods
  12. Boosting
  13. Neural Network
  14. Tricks and Philosophy in Deep Learning
  15. Deep Learning - Computer Vision Applications
  16. Deep Learning - Recurrent Neural Network
  17. Deep Learning - Recurrent Neural Network
  18. Reinforcement Learning in a Nutshell
  19. Generative Adversarial Networks and Autoencoders

All lectures and tutorials are found on Github.