We begin with a review of inference nets, then discuss how to use experimental data to develop a model, which can be used to perform simulations. If we have two competing models, we can use Bayes' rule to determine which is more likely to be accurate.

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. Neural Nets
13. Deep Neural Nets
14. Learning: Genetic Algorithms
15. Learning: Sparse Spaces, Phonology
16. Learning: Near Misses, Felicity Conditions
17. Learning: Support Vector Machines
18. Learning: Boosting
19. Representations: Classes, Trajectories, Transitions
20. Architectures: GPS, SOAR, Subsumption, Society of Mind
21. Probabilistic Inference I
22. Probabilistic Inference II
23. Model Merging, Cross-Modal Coupling, Course Summary
24. R1. Rule-Based Systems
25. R2. Basic Search, Optimal Search
26. R3. Games, Minimax, Alpha-Beta
27. R4. Neural Nets
28. R5. Support Vector Machines
29. R6. Boosting
30. R7. Near Misses, Arch Learning

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.