| Day | Topic | Reading | Slides | Out | Due | ||
| Tu 1/20 | Introduction to AI | Ch. 1, 2 | 2PP 6PP | Project 0: Tutorial | 1/28 | ||
| Th 1/22 | Agents and Search | Ch. 3 | 2PP 6PP | Project 1: Search | 2/4 | ||
| Tu 1/27 | A* Search and Heuristics | Ch. 4.1-2 | 2PP 6PP | ||||
| Th 1/29 | Constraint Satisfaction Problems | Ch. 5.1-2 | 2PP 6PP | Written 1: Search and CSPs | 2/10 | ||
| Tu 2/3 | CSPs II | Ch. 5.3-4 | 2PP 6PP | ||||
| Th 2/5 | Game Trees: Minimax | Ch. 6.2-5 | 2PP 6PP | Project 2: Multi-Agent Pacman | 2/18 | ||
| Tu 2/10 | Game Trees: Expectimax | Ch. 6.2-5 | 2PP 6PP | ||||
| Th 2/12 | Utility Theory | Ch. 16.1-3 | 2PP 6PP | ||||
| Tu 2/17 | Markov Decision Processes | Sutton and Barto: Ch. 3, 4 | 2PP 6PP | Project 3: Reinforcement Learning | 3/4 | ||
| Th 2/19 | MDPs II | Ch. 17.1-3; Sutton and Barto: Ch. 6.1 |
2PP 6PP | ||||
| Tu 2/24 | Reinforcement Learning | Sutton and Barto: Ch. 6.2, 6.5 | 2PP 6PP | ||||
| Th 2/26 | Reinforcement Learning II | Ch. 21.4; Sutton and Barto: Ch. 8.1 | 2PP 6PP | Written 2: MDPs and Bayes' Nets | 3/12 | ||
| Tu 3/3 | Probability | Ch. 13.1-6 | 2PP 6PP | ||||
| Th 3/5 | Bayes' Nets: Representation | Ch. 14.1-2, Applet | 2PP 6PP | ||||
| Tu 3/10 | Bayes' Nets: Independence | Ch. 14.3, D-separation | 2PP 6PP | ||||
| Th 3/12 | Bayes' Nets: Inference | Ch. 14.4-5 | 2PP 6PP | ||||
| Tu 3/17 | Bayes' Nets: Structure | Grapher example from class (optional) | 2PP 6PP | ||||
| Th 3/19 | Midterm Exam (6pm, Evans 10) and Midterm Review (in class) |
2PP 6PP | |||||
| Tu 3/24 |
Spring Break |
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| Th 3/26 |
Spring Break |
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| Tu 3/31 | Bayes' Nets: Sampling | Ch. 14.4-5 | 2PP 6PP | ||||
| Th 4/2 | Decision Diagrams | Ch. 15.1-3,6 | 2PP 6PP | Written 3: Probabilistic Models | 4/16 (extended) | ||
| Tu 4/7 | HMMs: Monitoring | Ch. 15.1, 15.2, 15.3, 15.5 | 2PP 6PP | Project 4: Ghostbusters | 4/22 | ||
| Th 4/9 | HMMs: Particle Filtering | Ch. 15.2, 15.5 | 2PP 6PP | ||||
| Tu 4/14 | HMMs for Speech Recognition | Ch. 15.2,15.6 | 2PP 6PP | ||||
| Th 4/16 | ML: Naive Bayes | 2PP 6PP | |||||
| Tu 4/21 | ML: Perceptron | 2PP 6PP | Written 4: Classification | 4/30 | |||
| Th 4/23 | ML: Perceptron and Kernels | 2PP 6PP | Project 5: Classification | 5/8 | |||
| Tu 4/28 | Natural Language Processing: Dan Klein | 2PP 6PP | |||||
| Th 4/30 | Robotics: Pieter Abbeel | Guest lecture | |||||
| Tu 5/5 | Unsupervised and Semi-supervised Learning: John Blitzer | 2PP 6PP | |||||
| Th 5/7 | Contest Finals and Advanced Topics | 2PP 6PP | |||||
| Tu 5/19 | Final Exam (8-11am, location TBD) |
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