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MIT·Patrick Winston
48:42
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MIT: Learning: Boosting Algorithms →#15 of 18
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Learning: Support Vector Machines
49:34Learning: Boosting Algorithms
51:40Learning: Near Misses and Felicity Conditions in AI
46:54Learning: Sparse Spaces and Phonological Generalization
47:49Genetic Algorithms: MIT Artificial Intelligence
47:16Deep Neural Networks: MIT Artificial Intelligence
49:06Neural Networks and Backpropagation
50:43Learning: Identification Trees and Disorder
49:37Introduction to Learning and Nearest Neighbors in AI
49:56Constraints in Visual Object Recognition: Alignment Theory and Face Recognition
51:32Constraints: Search and Domain Reduction
45:06Constraints: Interpreting Line Drawings
49:13Search: Games, Minimax, and Alpha-Beta Pruning
48:17Search: Optimal, Branch and Bound, A*
48:37Search: Depth-First, Hill Climbing, Beam Search
48:42Reasoning: Goal Trees and Rule-Based Expert Systems
49:56Reasoning: Goal Trees and Problem Solving
45:58Introduction and Scope of Artificial Intelligence
47:19Search: Depth-First, Hill Climbing, Beam Search
An MIT AI lecture exploring foundational search algorithms like depth-first and breadth-first, alongside refinements such as hill climbing and beam search.
Covers algorithms for depth-first and breadth-first search, followed by several refinements: keeping track of nodes already considered, hill climbing, and beam search. We end with a brief discussion of commonsense vs. reflective knowledge.