Course Content
Lecture 1:
Description: In this lecture, Dr. Bell introduces the theory of computation and explains some aspects of computational thinking. Programming languages are discussed, with an emphasis on basic Python syntax and data structures. Instructor: Dr. Ana Bell
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Introduction and Scope
47:19
Lecture 2:
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Reasoning: Goal Trees and Problem Solving
43:31
Lecture 3:
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Reasoning: Goal Trees and Rule-Based Expert Systems
45:01
Lecture 4:
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Search: Depth-First, Hill Climbing, Beam
41:08
Lecture 5:
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Search: Optimal, Branch and Bound, A*
41:27
Lecture 6:
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Search: Games, Minimax, and Alpha-Beta
48:21
Lecture 7:
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Constraints: Interpreting Line Drawings
41:32
Lecture 8:
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Constraints: Search, Domain Reduction
41:44
Lecture 9:
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Constraints: Visual Object Recognition
47:27
Lecture 10:
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Introduction to Learning, Nearest Neighbors
51:26
Lecture 11:
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Learning: Identification Trees, Disorder
49:12
Lecture 12:
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Deep Neural Nets
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Neural Nets
48:31
Lecture 13:
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Learning: Genetic Algorithms
00:00
Lecture 14:
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Learning: Sparse Spaces, Phonology
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Lecture 15:
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Learning: Near Misses, Felicity Conditions
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Lecture 16:
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Learning: Support Vector Machines
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Lecture 17:
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Learning: Boosting
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Lecture 18:
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Representations: Classes, Trajectories, Transitions
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Lecture 19:
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Architectures: GPS, SOAR, Subsumption, Society of Mind
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Lecture 20:
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Probabilistic Inference I
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Probabilistic Inference II
00:00
Lecture 21:
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Model Merging, Cross-Modal Coupling, Course Summary
00:00
Artificial Intelligence A-Z™: Learn How To Build An AI
About Lesson

Description: In this lecture, Prof. Winston introduces artificial intelligence and provides a brief history of the field. The last ten minutes are devoted to information about the course at MIT.

Instructor: Patrick H. Winston

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