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source: MIT OpenCourseWare Last updated on 2016年4月20日
MIT 6.034 Artificial Intelligence, Fall 2010
View the complete course: http://ocw.mit.edu/6-034F10
In these lectures, Prof. Patrick Winston introduces the 6.034 material from a conceptual, big-picture perspective. Topics include reasoning, search, constraints, learning, representations, architectures, and probabilistic inference.
License: Creative Commons BY-NC-SA
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1. Introduction and Scope 47:19
2. Reasoning: Goal Trees and Problem Solving 45:58
3. Reasoning: Goal Trees and Rule-Based Expert Systems 49:56
4. Search: Depth-First, Hill Climbing, Beam 48:42
5. Search: Optimal, Branch and Bound, A* 48:37
6. Search: Games, Minimax, and Alpha-Beta 48:17
7. Constraints: Interpreting Line Drawings 49:13
8. Constraints: Search, Domain Reduction 45:24
9. Constraints: Visual Object Recognition 51:32
10. Introduction to Learning, Nearest Neighbors 49:56
11. Learning: Identification Trees, Disorder 49:37
12. Learning: Neural Nets, Back Propagation 47:54
13. Learning: Genetic Algorithms 47:16
14. Learning: Sparse Spaces, Phonology 47:49
15. Learning: Near Misses, Felicity Conditions 46:54
16. Learning: Support Vector Machines 49:34
17. Learning: Boosting 51:40
18. Representations: Classes, Trajectories, Transitions 48:58
19. Architectures: GPS, SOAR, Subsumption, Society of Mind 49:06
21. Probabilistic Inference I 48:30
22. Probabilistic Inference II 48:46
23. Model Merging, Cross-Modal Coupling, Course Summary 49:31
Mega-R1. Rule-Based Systems 46:58
Mega-R2. Basic Search, Optimal Search 51:56
Mega-R3. Games, Minimax, Alpha-Beta 50:56
Mega-R4. Neural Nets 52:38
Mega-R5. Support Vector Machines 49:53
Mega-R6. Boosting 49:55
Mega-R7. Near Misses, Arch Learning 33:05
