Искусственный интеллект с Патриком Уинстоном

Artificial Intelligence, Fall 2010

1. Introduction and Scope

2. Reasoning: Goal Trees and Problem Solving

3. Reasoning: Goal Trees and Rule-Based Expert Systems

4. Search: Depth-First, Hill Climbing, Beam

5. Search: Optimal, Branch and Bound, A*

6. Search: Games, Minimax, and Alpha-Beta

7. Constraints: Interpreting Line Drawings

8. Constraints: Search, Domain Reduction

9. Constraints: Visual Object Recognition

10. Introduction to Learning, Nearest Neighbors

11. Learning: Identification Trees, Disorder

12. Learning: Neural Nets, Back Propagation

13. Learning: Genetic Algorithms

14. Learning: Sparse Spaces, Phonology

15. Learning: Near Misses, Felicity Conditions

16. Learning: Support Vector Machines

17. Learning: Boosting

18. Representations: Classes, Trajectories, Transitions

19. Architectures: GPS, SOAR, Subsumption, Society of Mind

21. Probabilistic Inference I

22. Probabilistic Inference II

23. Model Merging, Cross-Modal Coupling, Course Summary