MIT 6.034 Artificial Intelligence, Fall 2010

This course introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence. Created by MIT OpenCourseWare.

Average Course Length

30 hours

Skill Level


Pick a lesson

1: Introduction and Scope
2: Reasoning: Goal Trees and Problem Solving
3: Reasoning: Goal Trees and Rule-Based Expert Systems
4: Search: Depth-First, Hill Climbing, Beam
5: Search: Optimal, Branch and Bound, A*
6: Search: Games, Minimax, and Alpha-Beta
7: Constraints: Interpreting Line Drawings
8: Constraints: Search, Domain Reduction
9: Constraints: Visual Object Recognition
10: Introduction to Learning, Nearest Neighbors
11: Learning: Identification Trees, Disorder
12: Learning: Neural Nets, Back Propagation
13: Learning: Genetic Algorithms
14: Learning: Sparse Spaces, Phonology
15: Learning: Near Misses, Felicity Conditions
16: Learning: Support Vector Machines
17: Learning: Boosting
18: Representations: Classes, Trajectories, Transitions
19: Architectures: GPS, SOAR, Subsumption, Society of Mind
20: Probabilistic Inference I
21: Probabilistic Inference II
22: Model Merging, Cross-Modal Coupling, Course Summary
23: Mega-R1. Rule-Based Systems
24: Mega-R2. Basic Search, Optimal Search
25: Mega-R3. Games, Minimax, Alpha-Beta
26: Mega-R4. Neural Nets
27: Mega-R5. Support Vector Machines
28: Mega-R6. Boosting
29: Mega-R7. Near Misses, Arch Learning