| Day | Topic | Reading | Slides | Out | Due |
| Tu 1/19 | Introduction to AI | Ch. 1 (2nd Ed: Ch. 1) | 2PP 6PP | ||
| Th 1/21 | Agents and Search | Ch. 3.1-4 (2nd Ed: Ch. 3) | 2PP 6PP | W1: Search P0: Python Tutorial |
1/28 1/28 |
| Tu 1/26 | A* Search and Heuristics | Ch. 3.5-6 (2nd Ed: Ch. 4.1-2) | 2PP 6PP | ||
| Th 1/28 | Constraint Satisfaction Problems | Ch. 6.1 (2nd Ed: Ch. 5.1) | 2PP 6PP 2PP++ 6PP++ | P1: Search | 2/4 |
| Tu 2/2 | CSPs II | Ch. 6.2-5 (2nd Ed: Ch. 5.2-4) | 2PP 6PP 2PP++ 6PP++ | ||
| Th 2/4 | Game Trees: Minimax | Ch. 5.2-5 (2nd Ed: Ch. 6.2-5) | 2PP 6PP 2PP++ 6PP++ | W2: CSP's and Game Trees | 2/11 |
| Tu 2/9 | Game Trees: Expectimax | Ch. 5.2-5 (2nd Ed: Ch. 6.2-5) | 2PP 6PP 2PP++ 6PP++ | ||
| Th 2/11 | Utility Theory | Ch. 16.1-3 (2nd Ed: Ch. 16.1-3) | 2PP 6PP 2PP++ 6PP++ | P2: Multi-Agent Pacman | 2/18 |
| Tu 2/16 | Markov Decision Processes | Sutton and Barto Ch. 3-4 | 2PP 6PP 2PP++ 6PP++ | ||
| Th 2/18 | MDPs II | Ch. 17.1-3 (2nd Ed: Ch. 17.1-3), Sutton and Barto Ch. 6.1,2,5 | 2PP 6PP 2PP++ 6PP++ | W3: Expectimax & Utility | 2/25 |
| Tu 2/23 | Reinforcement Learning | 2PP 6PP 2PP++ 6PP++ | |||
| Th 2/25 | Reinforcement Learning II | 2PP 6PP 2PP++ 6PP++ | P3: Reinforcement Learning | 3/4 | |
| Tu 3/2 | Probability | Ch. 13.1-5 (2nd Ed: Ch. 13.1-6) | 2PP 6PP 2PP++ 6PP++ | ||
| Th 3/4 | Bayes' Nets: Representation | Ch. 14.1-2,4 (2nd Ed: Ch. 14.1-2,4) | 2PP 6PP 2PP++ 6PP++ | W4: MDPs and Probability | 3/11 |
| Tu 3/9 | Bayes' Nets: Independence | Ch. 14.3 (2nd Ed: Ch. 14.3), Jordan 2.1 | 2PP 6PP 2PP++ 6PP++ | ||
| Th 3/11 | Bayes' Nets: Inference | Ch. 14.4-5 (2nd Ed: Ch. 14.4-5) | 2PP 6PP 2PP++ 6PP++ | Nothing going out, preptime for midterm. | |
| Tu 3/16 | Bayes' Nets: Sampling | Ch. 14.4-5 (2nd Ed: Ch. 14.4-5) | 2PP 6PP 2PP++ 6PP++ | ||
| Th 3/18 | Midterm Exam: 6pm-9pm, | 0010 Evans (NO LECTURE) | |||
| Tu 3/23 |
Spring Break |
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| Th 3/25 |
Spring Break |
Written 5 | 4/1 | ||
| Tu 3/30 | Sampling and Decision Diagrams | Ch. 15.1-3, 6 (2nd Ed: Ch. 15.1-3,6) | 2PP 6PP 2PP++ 6PP++ | ||
| Th 4/1 | HMMs: Filtering | Ch. 15.2,5 (2nd Ed: Ch. 15.2,5) | 2PP 6PP 2PP++ 6PP++ | Written 6 | 4/8 |
| Tu 4/6 | HMMs: Particle Filtering | Ch. 15.2,6 (2nd Ed: Ch. 15.2,6) | 2PP 6PP 2PP++ 6PP++ | ||
| Th 4/8 | DBNs / Viterbi / Speech | Ch. 15.2,6 (2nd Ed: Ch. 15.2,6) | 2PP 6PP 2PP++ 6PP++ | P4: Ghostbusters | 4/15 |
| Tu 4/13 | ML: Naive Bayes | 2PP 6PP 2PP++ 6PP++ | |||
| Th 4/15 | ML: Perceptrons | 2PP 6PP 2PP++ 6PP++ | Written 7 | 4/22 | |
| Tu 4/20 | ML: Perceptrons, SVM | 2PP 6PP 2PP++ 6PP++ | |||
| Th 4/22 | ML: nearest neighbor, kernels | 2PP 6PP 2PP++ 6PP++ | P5: Classification | 4/29 | |
| Tu 4/27 | Robotics | 2PP 6PP | |||
| Th 4/29 | Vision, Language, Conclusion | 2PP 6PP | |||
| Th 5/13 | Final Exam (3-6pm, 120 Latimer ) |
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