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MIT 6.034 Artificial Intelligence, Fall 2010

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30 items
Last updated on Apr 23, 2021
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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
45:06
9. Constraints: Visual Object Recognition
51:32
10. Introduction to Learning, Nearest Neighbors
49:56
11. Learning: Identification Trees, Disorder
49:37
12a: Neural Nets
50:43
12b: Deep Neural Nets
49:06
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
33:04