Artificial Intelligence Complete Courses (01-23) Patrick H. Winston

Prof. Patrick Henry Winston introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence.

Upon completion of this course, students should be able to develop intelligent systems by assembling solutions to concrete computational problems; understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering; and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective.

Who is Patrick H. Winston?
Patrick H. Winston is Ford Professor of Artificial Intelligence and Computer Science at the Massachusetts Institute of Technology. He has been with CSAIL and before that the MIT Artificial Intelligence Laboratory since 1967. He joined the faculty in 1970, and he was the Director of the Artificial Intelligence Laboratory from 1972 to 1997.

Professor Winston is particularly involved in the study of how vision, language, and motor faculties account for intelligence. He also works on applications of Artificial Intelligence that are enabled by learning, precedent-based reasoning, and common-sense problem solving.


Professor Winston is chairman and cofounder of Ascent Technology, Inc., a company that produces sophisticated scheduling, resource allocation, and schedule recovery applications, enabled by AI technology, and in use throughout the world in major airports and the Department of Defense.

Professor Winston was a member of the Naval Research Advisory Committee (NRAC) (1985-1990, 1994-2000) for which he served as Chair from 1997 to 2000. During his service on NRAC, he chaired several studies, including a study of how the Navy can best exploit the next generation of computer resources and a study of technology for reduced manning. Professor Winston is also a past president of the American Association for Artificial Intelligence.

Professor Winston is working on a major new research and educational effort, the Human Intelligence Enterprise, which will bring together and focus research from several fields, including Computer Science, Systems Neuroscience, Cognitive Science, and Linguistics.

Artificial Intelligence Lectures – 01
Introduction and Scope

Artificial Intelligence Lectures – 02
Reasoning: Goal Trees and Problem Solving

Artificial Intelligence Lectures – 03
Reasoning: Goal Trees and Rule-Based Expert Systems

Artificial Intelligence Lectures – 04
Search: Depth-First, Hill Climbing, Beam

Artificial Intelligence Lectures – 05
Search: Optimal, Branch and Bound, A*

Artificial Intelligence Lectures – 06
Search: Games, Minimax, and Alpha-Beta

Artificial Intelligence Lectures – 07
Constraints: Interpreting Line Drawings

Artificial Intelligence Lectures – 08
Constraints: Search, Domain Reduction

Artificial Intelligence Lectures – 09
Constraints: Visual Object Recognition

Artificial Intelligence Lectures – 10
Introduction to Learning, Nearest Neighbors

Artificial Intelligence Lectures – 11
Learning: Identification Trees, Disorder

Artificial Intelligence Lectures – 12a
Neural Nets

Artificial Intelligence Lectures – 12b
Deep Neural Nets

Artificial Intelligence Lectures – 13
Learning: Genetic Algorithms

Artificial Intelligence Lectures – 14
Learning: Sparse Spaces, Phonology

Artificial Intelligence Lectures – 15
Learning: Near Misses, Felicity Conditions

Artificial Intelligence Lectures – 16
Learning: Support Vector Machines

Artificial Intelligence Lectures – 17
Learning: Boosting

Artificial Intelligence Lectures – 18
Representations: Classes, Trajectories, Transitions

Artificial Intelligence Lectures – 19
Architectures: GPS, SOAR, Subsumption, Society of Mind

Lecture 20, which focuses on the AI business, is not available in MIT Lecture Videos due to unknown reasons..

Artificial Intelligence Lectures – 21
Probabilistic Inference I

Artificial Intelligence Lectures – 22
Probabilistic Inference II

Artificial Intelligence Lectures – 23
Model Merging, Cross-Modal Coupling, Course Summary

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