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MIT OpenCourseWare · series 4 of 10

6.034 Artificial Intelligence

Patrick Winston's classic course: search, constraints, learning, neural nets, support vector machines and probabilistic inference, with the Mega-Recitations. Fall 2010.

30 lectures0 min

Episodes

  1. Lec 1

    Introduction and ScopeNot yet

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  2. Lec 2

    Reasoning: Goal Trees and Problem SolvingNot yet

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  3. Lec 3

    Reasoning: Goal Trees and Rule-Based Expert SystemsNot yet

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  4. Lec 4

    Search: Depth-First, Hill Climbing, BeamNot yet

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  5. Lec 5

    Search: Optimal, Branch and Bound, A*Not yet

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  6. Lec 6

    Search: Games, Minimax, and Alpha-BetaNot yet

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  7. Lec 7

    Constraints: Interpreting Line DrawingsNot yet

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  8. Lec 8

    Constraints: Search, Domain ReductionNot yet

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  9. Lec 9

    Constraints: Visual Object RecognitionNot yet

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  10. Lec 10

    Introduction to Learning, Nearest NeighborsNot yet

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  11. Lec 11

    Learning: Identification Trees, DisorderNot yet

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  12. Lec 12a

    Neural NetsNot yet

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  13. Lec 12b

    Deep Neural NetsNot yet

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  14. Lec 13

    Learning: Genetic AlgorithmsNot yet

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  15. Lec 14

    Learning: Sparse Spaces, PhonologyNot yet

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  16. Lec 15

    Learning: Near Misses, Felicity ConditionsNot yet

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  17. Lec 16

    Learning: Support Vector MachinesNot yet

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  18. Lec 17

    Learning: BoostingNot yet

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  19. Lec 18

    Representations: Classes, Trajectories, TransitionsNot yet

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  20. Lec 19

    Architectures: GPS, SOAR, Subsumption, Society of MindNot yet

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  21. Lec 21

    Probabilistic Inference INot yet

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  22. Lec 22

    Probabilistic Inference IINot yet

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  23. Lec 23

    Model Merging, Cross-Modal Coupling, Course SummaryNot yet

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  24. Mega-R1

    Rule-Based SystemsNot yet

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  25. Mega-R2

    Basic Search, Optimal SearchNot yet

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  26. Mega-R3

    Games, Minimax, Alpha-BetaNot yet

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  27. Mega-R4

    Neural NetsNot yet

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  28. Mega-R5

    Support Vector MachinesNot yet

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  29. Mega-R6

    BoostingNot yet

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  30. Mega-R7

    Near Misses, Arch LearningNot yet

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