6. Search: Games; Minimax; and Alpha-Beta
, 18. Representations: Classes; Trajectories; Transitions
, 9. Constraints: Visual Object Recognition
, Mega-R5. Support Vector Machines
, 14. Learning: Sparse Spaces; Phonology
, 19. Architectures: GPS; SOAR; Subsumption; Society of Mind
, Mega-R1. Rule-Based Systems
, 17. Learning: Boosting
, Mega-R7. Near Misses; Arch Learning
, Mega-R6. Boosting
, Mega-R3. Games; Minimax; Alpha-Beta
, 13. Learning: Genetic Algorithms
, 15. Learning: Near Misses; Felicity Conditions
, 12b: Deep Neural Nets
, 22. Probabilistic Inference II
, Mega-R4. Neural Nets
, 8. Constraints: Search; Domain Reduction
, 5. Search: Optimal; Branch and Bound; A*
, 16. Learning: Support Vector Machines
, 23. Model Merging; Cross-Modal Coupling; Course Summary
, 12a: Neural Nets
, 4. Search: Depth-First; Hill Climbing; Beam
, Mega-R2. Basic Search; Optimal Search
, 2. Reasoning: Goal Trees and Problem Solving
, 3. Reasoning: Goal Trees and Rule-Based Expert Systems
, 7. Constraints: Interpreting Line Drawings
, 10. Introduction to Learning; Nearest Neighbors
, 21. Probabilistic Inference I
, 1. Introduction and Scope