AI:
1. Introduction to AI Ch. 1 [Project 0 out]
2. Agents and Search (Review) Ch. 3.1-4 (2e: Ch. 3)
3. A* Search and Heuristics Ch. 3.5-6 (2e: Ch. 4.1-2)
4. Constraint Satisfaction Problems Ch. 6.1 (2e: Ch. 5.1)
5. Constraint Satisfaction Problems II Ch. 6.2-5 (2e: Ch. 5.2-4)
6. Game Trees: Minimax Ch. 5.2-5 (2e: Ch. 6.2-5)
7. Game Trees: Expectimax / Probability Review Ch. 16.1-3
8. Utility Theory Ch. 16.1-3

AI / Reinforcement Learning:
9. Markov Decision Processes (Review) Sutton and Barto Ch. 3-4  
10. MDPs II Ch. 17.1-3, S&B Ch. 6.1,2,5  
11. Reinforcement Learning I  Sutton and Barto (Ch. TBD)
12. Reinforcement Learning II Sutton and Barto (Ch. TBD)  

Statistical Machine Learning:
13. Probability (Review) Ch. 13.1-5 (2e: Ch. 13.1-6)  
14. Bayes’ Nets: Representation Ch. 14.1-2,4
15. Bayes’ Nets: Independence Ch. 14.3
16. Bayes’ Nets: Inference Ch. 14.4-5  
17. Bayes’ Nets: Sampling Ch. 14.4-5    
18. Decision Diagrams / VPI Ch. 15.1-3, 6
19. HMMs: Filtering Ch. 15.2,5
20. HMMs: Particle Filtering Ch. 15.2,6
21. Speech / Viterbi / DBNs Ch. 15.2,6
22. Naive Bayes