| Lecture 1. Introduction. Artificial General Intelligence | PDF, Appendix 1, Appendix 2 |
| Lecture 2. Difficulties and features of simulation of mind and brain | |
| Lecture 3. Logical approach to AI | |
| Lecture 4. Knowledge representation and inference | |
| Lecture 5. Computational Intelligence. Neural Networks. | PDF, Part 2, Part 3, Part 4 |
| Lecture 6. Evolution Programming | |
| Lecture 7. Concept of hybrid intelligent system (HIS). Taxonomy of HIS | |
| Lecture 8. Hybrid Expert Systems | |
| Lecture 9. ESWin - toolkit for development of hybrid expert systems | |
| Lecture 10. Rule extraction from neural networks | |
| Lecture 11. Representation of knowledge by neural networks. Fuzzy neural networks | |
| Lecture 12. Using of Genetic Algorithms for learning and evolution of neural networks | |
| Lecture 13. Introduction to Natural Language Processing. | |
| Lecture 14. Neural networks and hybrid approach in natural language processing | PDF, Part 2, Appendix 1, Appendix 2 |
| Lecture 15. Neural Networks in Robotics | PDF, Appendix 1, Appendix 2 |
| Lecture 16. Hybrid Information Systems for Robots | |
| Lecture 17. Future of HIS and AGI. Emotions, motivation, consciousness | PDF, Appendix 1, Appendix 2, Appendix 3, Appendix 4, Appendix 5, Appendix 6, |
| Colloquium 1. Invariant Clustering and Recognition in Dynamic Environment by Hybrid Neural Network MLP-ART | |
| Colloquium 2. Introduction to methodology for development of HIS | PDF, Appendix |