| Lecture 1. Introduction to Machine Learning | |
| Lecture 2. Concept Learning | |
| Lecture 3. Computational Learning Theory | |
| Lecture 4. Decision Tree Learning | |
| Lecture 5. ArtificialNeural Networks. Multi-layer perceptrons. Error back propagation | |
| Lecture 6. Unsupervised learning. Clustering. Self-organizing maps of Kohonen | |
| Lecture 7. Adaptive resonance theory. Models ART-1, ART-2, Fuzzy-ART, ARTMAP | |
| Lecture 8. Associative memory based on neural networks. Hopfield model. | |
| Lecture 9. Recurrent multi-layer neural networks | |
| Lecture 10. Evolution Programming and Genetic Algorithms | |
| Lecture 11. Using of Genetic Algorithms for learning and evolution of neural networks | |
| Lecture 12. Instance Based Learning. Radial basis functions | |
| Lecture 13. Bayesian Learning | |
| Lecture 14. Support Vector Machines | |
| Lecture 15. Reinforcement Learning | |
| Lecture 16. Application of RL | PDF, Appendix |
| Lecture 17. Learning in Natural Language Processing (NLP). Problems and main concepts of NLP. | |
| Lecture 18. Learning in Natural Language Processing (NLP). Methods of simulation of understanding of NL | PDF, Appendix 1, Appendix 2, Appendix 3 |
| Lecture 19. Long-Short Term Memory (LSTM) | |
| Colloquium 1. Features of construction and working of brain | |
| Colloquium 2. An Approach for Invariant Clustering and Recognition in Dynamic Environment |