Emby / MIT - Artificial Intelligence (6.034)
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
12a - Neural Nets
12b - Deep Neural Nets
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
Mega-R1. Rule-Based Systems
Mega-R2. Basic Search, Optimal Search
Mega-R3. Games, Minimax, Alpha-Beta
Mega-R4. Neural Nets
Mega-R5. Support Vector Machines
Mega-R6. Boosting
Mega-R7. Near Misses, Arch Learning