Artificial Intelligence

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.

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.
12a: Neural Nets.
12b: Deep Neural Nets.
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.
21. Probabilistic Inference I.
22. Probabilistic Inference II.
23. Model Merging, Cross-Modal Coupling, Course Summary.
Mega-R1. Rule-Based Systems.
Mega-R2. Basic Search, Optimal Search.
Mega-R3. Games, Minimax, Alpha-Beta.
Mega-R4. Neural Nets.
Mega-R5. Support Vector Machines.
Mega-R6. Boosting.
Mega-R7. Near Misses, Arch Learning.