Learn · Artificial Intelligence

Lecture 10 of 30Lecture Videos50 min

Lecture 10: Introduction to Learning, Nearest Neighbors

Patrick Henry Winston · Massachusetts Institute of Technology

At MIT OpenCourseWare Transcript

Credits and licence

From
Artificial Intelligence, Massachusetts Institute of Technology (at MIT OpenCourseWare)
Licence
CC BY-NC-SA 4.0, as MIT OpenCourseWare states it. Non-commercial use only.
Attribution
Patrick Henry Winston. Artificial Intelligence. Fall 2010. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu/. License: Creative Commons BY-NC-SA.
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All the lectures

  1. Lecture 1: Introduction and Scope 48 min · Video
  2. Lecture 2: Reasoning: Goal Trees and Problem Solving 46 min · Video
  3. Lecture 3: Reasoning: Goal Trees and Rule-Based Expert Systems 50 min · Video
  4. Lecture 4: Search: Depth-First, Hill Climbing, Beam 49 min · Video
  5. Lecture 5: Search: Optimal, Branch and Bound, A* 49 min · Video
  6. Lecture 6: Search: Games, Minimax, and Alpha-Beta 49 min · Video
  7. Lecture 7: Constraints: Interpreting Line Drawings 50 min · Video
  8. Lecture 8: Constraints: Search, Domain Reduction 46 min · Video
  9. Lecture 9: Constraints: Visual Object Recognition 52 min · Video
  10. Lecture 10: Introduction to Learning, Nearest Neighbors 50 min · Video
  11. Lecture 11: Learning: Identification Trees, Disorder 50 min · Video
  12. Lecture 12A: Neural Nets 51 min · Video
  13. Lecture 12B: Deep Neural Nets 50 min · Video
  14. Lecture 13: Learning: Genetic Algorithms 48 min · Video
  15. Lecture 14: Learning: Sparse Spaces, Phonology 48 min · Video
  16. Lecture 15: Learning: Near Misses, Felicity Conditions 47 min · Video
  17. Lecture 16: Learning: Support Vector Machines 50 min · Video
  18. Lecture 17: Learning: Boosting 52 min · Video
  19. Lecture 18: Representations: Classes, Trajectories, Transitions 49 min · Video
  20. Lecture 19: Architectures: GPS, SOAR, Subsumption, Society of Mind 50 min · Video
  21. Lecture 21: Probabilistic Inference I 49 min · Video
  22. Lecture 22: Probabilistic Inference II 49 min · Video
  23. Lecture 23: Model Merging, Cross-Modal Coupling, Course Summary 50 min · Video
  24. Mega-Recitation 1: Rule-Based Systems 47 min · Video
  25. Mega-Recitation 2: Basic Search, Optimal Search 52 min · Video
  26. Mega-Recitation 3: Games, Minimax, Alpha-Beta 51 min · Video
  27. Mega-Recitation 4: Neural Nets 53 min · Video
  28. Mega-Recitation 5: Support Vector Machines 50 min · Video
  29. Mega-Recitation 6: Boosting 50 min · Video
  30. Mega-Recitation 7: Near Misses, Arch Learning 34 min · Video