6.034 Artificial Intelligence

6.034 Artificial Intelligence (Fall 2010, MIT OCW). Instructor: Professor Patrick Henry Winston. This course introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence. Upon completion of 6.034, 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. (from


Lecture 01 - Introduction and Scope
Lecture 02 - Reasoning: Goal Trees and Problem Solving
Lecture 03 - Reasoning: Goal Trees and Rule-Based Expert Systems
Lecture 04 - Search: Depth-First, Hill Climbing, Beam
Lecture 05 - Search: Optimal, Branch and Bound, A*
Lecture 06 - Search: Games, Minimax, and Alpha-Beta
Lecture 07 - Constraints: Interpreting Line Drawings
Lecture 08 - Constraints: Search, Domain Reduction
Lecture 09 - Constraints: Visual Object Recognition
Lecture 10 - Introduction to Learning, Nearest Neighbors
Lecture 11 - Learning: Identification Trees, Disorder
Lecture 12 - Learning: Neural Nets, Back Propagation
Lecture 13 - Learning: Genetic Algorithms
Lecture 14 - Learning: Sparse Spaces, Phonology
Lecture 15 - Learning: Near Misses, Felicity Conditions
Lecture 16 - Learning: Support Vector Machines
Lecture 17 - Learning: Boosting
Lecture 18 - Representations: Classes, Trajectories, Transitions
Lecture 19 - Architectures: GPS, SOAR, Subsumption, Society of Mind
Lecture 20
Lecture 21 - Probabilistic Inference I
Lecture 22 - Probabilistic Inference II
Lecture 23 - Model Merging, Cross-Modal Coupling, Course Summary

Artificial Intelligence (Fall 2010)
Instructor: Professor Patrick Henry Winston. Readings. Recitation Videos. Tutorials. Exams (no solutions). Assignments (no solutions). This course includes interactive demonstrations.