01. Definition and main properties of learning algorithms 02. The basic notions of machine learning 03. The basics of Bayes decision theory 04. The naive Bayes model 04. Fitting Gauss using the ML and the Bayes method 05. GMM model and its training using the EM algorithm 06. Non-parametric methods 07. Decision trees - the ID3 algorithm 08. Decision trees - extensions, modifications 09. Linear classifiers 10. Linear SVMs 11. Non-linear SVMs 12. Ensemble learning 13. Feature space transformation methods 14. PAC learning, efficient PAC learning 15. Occam learning, sample complexity 16. Artificial neural networks 17. Deep neural networks