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.
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