Artificial Intelligence (MIT 6.034)

Intermediate Computer Science by Y. Tian. 29 lectures, 23.5 hours. Covers: search constraint propagation learning neural nets genetic algorithms.

Lectures

  1. 1. Introduction and Scope · 47 min
  2. 2. Reasoning: Goal Trees and Problem Solving · 46 min
  3. 3. Reasoning: Goal Trees and Rule-Based Expert Systems · 50 min
  4. 4. Search: Depth-First, Hill Climbing, Beam · 49 min
  5. 5. Search: Optimal, Branch and Bound, A* · 49 min
  6. 6. Search: Games, Minimax, and Alpha-Beta · 48 min
  7. 7. Constraints: Interpreting Line Drawings · 49 min
  8. 9. Constraints: Visual Object Recognition · 52 min
  9. 10. Introduction to Learning, Nearest Neighbors · 50 min
  10. 11. Learning: Identification Trees, Disorder · 50 min
  11. 12a: Neural Nets · 51 min
  12. 12b: Deep Neural Nets · 49 min
  13. 13. Learning: Genetic Algorithms · 47 min
  14. 14. Learning: Sparse Spaces, Phonology · 48 min
  15. 15. Learning: Near Misses, Felicity Conditions · 47 min
  16. 16. Learning: Support Vector Machines · 50 min
  17. 17. Learning: Boosting · 52 min
  18. 18. Representations: Classes, Trajectories, Transitions · 49 min
  19. 19. Architectures: GPS, SOAR, Subsumption, Society of Mind · 49 min
  20. 21. Probabilistic Inference I · 49 min
  21. 22. Probabilistic Inference II · 49 min
  22. 23. Model Merging, Cross-Modal Coupling, Course Summary · 50 min
  23. Mega-R1. Rule-Based Systems · 47 min
  24. Mega-R2. Basic Search, Optimal Search · 52 min
  25. Mega-R3. Games, Minimax, Alpha-Beta · 51 min
  26. Mega-R4. Neural Nets · 53 min
  27. Mega-R5. Support Vector Machines · 50 min
  28. Mega-R6. Boosting · 50 min
  29. Mega-R7. Near Misses, Arch Learning · 33 min

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