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Reviews (4)

Onyeka Obi
Christian Komonen
ariel smith
Nuno Goncalves


This course introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence. Upon completion of the 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.

Over 25 hours of video;

23 lecture videos;

6 assignments and mega-recitations.


Course book

Winston, Patrick Henry. Artificial Intelligence. 3rd ed. Addison-Wesley, 1992.


Patrick Henry 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. 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 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.


Creative Commons License

Artificial Intelligence by Partik Winston is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike. Based on work at MIT Open CourseWare.

Course content

  • Reasoning: Goal Trees and Problem Solving

  • Reasoning: Goal Trees and Rule-Based Expert Systems

  • Search: Depth-First, Hill Climbing, Beam

  • Search: Optimal, Branch and Bound, A*

  • Search: Games, Minimax, and Alpha-Beta

  • Constraints: Interpreting Line Drawings

  • Constraints: Search, Domain Reduction

  • Constraints: Visual Object Recognition

  • Introduction to Learning, Nearest Neighbors

  • Learning: Identification Trees, Disorder

  • Learning: Neural Nets, Back Propagation

  • Learning: Genetic Algorithms

  • Learning: Sparse Spaces, Phonology

  • Learning: Near Misses, Felicity Conditions

  • Learning: Support Vector Machines

  • Learning: Boosting

  • Representations: Classes, Trajectories, Transitions

  • Architectures: GPS, SOAR, Subsumption, Society of Mind

  • Probabilistic Inference I

  • Probabilistic Inference II

  • Model Merging, Cross-Modal Coupling, Course Summary

  • Appendix: Mega-Recitation

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