Artificial Intelligence (Fall 2010)

Artificial Intelligence (Fall 2010)

Prof. Patrick Henry Winston via MIT OpenCourseWare Direct link

14. Learning: Sparse Spaces, Phonology

15 of 30

15 of 30

14. Learning: Sparse Spaces, Phonology

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Classroom Contents

Artificial Intelligence (Fall 2010)

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

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