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Artificial Intelligence
Lecture Videos
Lecture 1: Introduction and Scope (47:18)
Lecture 2: Reasoning: Goal Trees and Problem Solving (45:57)
Lecture 3: Reasoning: Goal Trees and Rule-Based Expert Systems (49:55)
Lecture 4: Search: Depth-First, Hill Climbing, Beam (48:41)
Lecture 5: Search: Optimal, Branch and Bound, A* (48:36)
Lecture 6: Search: Games, Minimax, and Alpha-Beta (48:16)
Lecture 7: Constraints: Interpreting Line Drawings (49:12)
Lecture 8: Constraints: Search, Domain Reduction (45:23)
Lecture 9: Constraints: Visual Object Recognition (51:31)
Lecture 10: Introduction to Learning, Nearest Neighbors (49:55)
Lecture 11: Learning: Identification Trees, Disorder
Lecture 12: Learning: Neural Nets, Back Propagation (47:53)
Lecture 13: Learning: Genetic Algorithms (47:15)
Lecture 14: Learning: Sparse Spaces, Phonology (47:48)
Lecture 15: Learning: Near Misses, Felicity Conditions (46:53)
Lecture 16: Learning: Support Vector Machines (49:33)
Lecture 17: Learning: Boosting (51:39)
Lecture 18: Representations: Classes, Trajectories, Transitions (48:57)
Lecture 19: Architectures: GPS, SOAR, Subsumption, Society of Mind (49:05)
Lecture 20: Probabilistic Inference I (48:29)
Lecture 21: Probabilistic Inference II (48:45)
Lecture 22: Model Merging, Cross-Modal Coupling, Course Summary (49:30)
Readings
Lecture A : Learning: neural nets, back propagation
Lecture B : Learning: sparse spaces, phonology
Lecture C : Learning: support vector machines
Lecture D : Learning: boosting
Lecture E : Architectures: GPS, SOAR, Subsumption, Society of Mind
Lecture F : Probabilistic inference II
Lecture G : Model merging, cross-modal coupling, course summary
Assignments
Problem Set 0
Problem Set 1
Problem Set 2
Problem Set 3
Problem Set 4
Problem Set 5
Exams
Fall 2010 Exams
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Fall 2009 Exams
Fall 2008 Exams
Fall 2007 Exams
Lecture D : Learning: boosting
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