Independent academic archive · Rights remain with the original publishers · Streamed via YouTube
Independent academic archive · Rights remain with the original publishers · Streamed via YouTube

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
47:19A lecture from MIT's AI course on optimal pathfinding strategies, including branch and bound, admissible heuristics, and the A* algorithm, with a focus on consistency.
Ford Professor of Artificial Intelligence and Computer Science
Longtime Director of the MIT Artificial Intelligence Laboratory celebrated for MIT 6.034 Artificial Intelligence and his iconic 'How to Speak' lecture.
Patrick Henry Winston
Patrick Henry Winston's classic MIT 6.034 textbook uniting symbolic reasoning, heuristic search, constraint propagation, and machine learning.
48:42Patrick Winston · MIT
1:23:34Erik Demaine & Srini Devadas · MIT
1:25:00Pieter Abbeel · UC Berkeley
53:21Srini Devadas · MIT
45:06Patrick Winston · MIT
Parallel graduate curricula in Algorithms & Data Structures taught at Carnegie Mellon University (Ryan O'Donnell) and MIT (Patrick Winston).
MIT 6.006: Introduction to Algorithms (MIT) establishes the undergraduate foundations in Algorithms & Data Structures required for MIT: Learning: Boosting Algorithms (MIT).
MIT 6.034: Artificial Intelligence (MIT) establishes the undergraduate foundations in Algorithms & Data Structures required for MIT: Learning: Boosting Algorithms (MIT).
MIT 6.046J: Design and Analysis of Algorithms (MIT) establishes the undergraduate foundations in Algorithms & Data Structures required for MIT: Learning: Boosting Algorithms (MIT).
55:25MIT