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