Artificaial Intelligence (M-I-T)

• 1. Introduction and Scope (M-I-T)
• 2. Reasoning: Goal Trees and Problem Solving (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)
• 7. Constraints: Interpreting Line Drawings (M-I-T)
• 8. Constraints: Search, Domain Reduction (M-I-T)
• 9. Constraints: Visual Object Recognition (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)
• 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)
• Mega-R1. Rule-Based Systems (M-I-T)
• Mega-R2. Basic Search, Optimal Search (M-I-T)
• Mega-R3. Games, Minimax, Alpha-Beta (M-I-T)