Artificaial Intelligence (M-I-T)

1. Introduction and Scope (M-I-T)

10. Introduction to Learning, Nearest Neighbors (M-I-T)

11. Learning: Identification Trees, Disorder (M-I-T)

12a: Neural Nets (M-I-T)

12b: Deep Neural Nets (M-I-T)

13. Learning: Genetic Algorithms (M-I-T)

14. Learning: Sparse Spaces, Phonology (M-I-T)

15. Learning: Near Misses, Felicity Conditions (M-I-T)

16. Learning: Support Vector Machines (M-I-T)

17. Learning: Boosting (M-I-T)

18. Representations: Classes, Trajectories, Transitions (M-I-T)

19. Architectures: GPS, SOAR, Subsumption, Society of Mind (M-I-T)

2. Reasoning: Goal Trees and Problem Solving (M-I-T)

21. Probabilistic Inference I (M-I-T)

22. Probabilistic Inference II (M-I-T)

23. Model Merging, Cross-Modal Coupling, Course Summary (M-I-T)

3. Reasoning: Goal Trees and Rule-Based Expert Systems (M-I-T)

4. Search: Depth-First, Hill Climbing, Beam (M-I-T)

5. Search: Optimal, Branch and Bound, A* (M-I-T)

6. Search: Games, Minimax, and Alpha-Beta (M-I-T)

7. Constraints: Interpreting Line Drawings (M-I-T)

8. Constraints: Search, Domain Reduction (M-I-T)

9. Constraints: Visual Object Recognition (M-I-T)

Mega-R1. Rule-Based Systems (M-I-T)

Mega-R2. Basic Search, Optimal Search (M-I-T)