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