Artificial Intelligence
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Lecture 1: Introduction and Scope (47:18)
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Lecture 2: Reasoning: Goal Trees and Problem Solving (45:57)
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Lecture 3: Reasoning: Goal Trees and Rule-Based Expert Systems (49:55)
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Lecture 4: Search: Depth-First, Hill Climbing, Beam (48:41)
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Lecture 5: Search: Optimal, Branch and Bound, A* (48:36)
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Lecture 6: Search: Games, Minimax, and Alpha-Beta (48:16)
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Lecture 7: Constraints: Interpreting Line Drawings (49:12)
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Lecture 8: Constraints: Search, Domain Reduction (45:23)
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Lecture 9: Constraints: Visual Object Recognition (51:31)
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Lecture 10: Introduction to Learning, Nearest Neighbors (49:55)
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Lecture 11: Learning: Identification Trees, Disorder (49:36)
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Lecture 12A: Neural Nets
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Lecture 12B: Deep Neural Nets
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Lecture 13: Learning: Genetic Algorithms (47:15)
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Lecture 14: Learning: Sparse Spaces, Phonology (47:48)
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Lecture 15: Learning: Near Misses, Felicity Conditions (46:53)
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Lecture 16: Learning: Support Vector Machines (49:33)
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Lecture 17: Learning: Boosting (51:39)
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Lecture 18: Representations: Classes, Trajectories, Transitions (48:57)
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Lecture 19: Architectures: GPS, SOAR, Subsumption, Society of Mind (49:05)
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Lecture 21: Probabilistic Inference I (48:29)
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Lecture 22: Probabilistic Inference II (48:45)
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Lecture 23: Model Merging, Cross-Modal Coupling, Course Summary (49:30)
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Problem sets
Mega-Recitation Videos
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Mega-Recitation 1: Rule-Based Systems (46:57)
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Mega-Recitation 2: Basic Search, Optimal Search (51:55)
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Mega-Recitation 3: Games, Minimax, Alpha-Beta (50:55)
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Mega-Recitation 4: Neural Nets (52:37)
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Mega-Recitation 5: Support Vector Machines (49:52)
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Mega-Recitation 6: Boosting (49:54)
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Mega-Recitation 7: Near Misses, Arch Learning (33:03)
