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If you would like to see previous term's slides (there will be changes), you can take a look here:
Fall 06 or
Fall 07
| Day | Topic | Reading | Slides | Out | Due |
| Tu 1/22 | Introduction to AI | Python Tutorial | 1/31 | ||
| Th 1/24 | Probability | Ch 13.1-6 | |||
| Tu 1/29 | Probability | Ch 13.1-6 | |||
| Th 1/31 | Probability, Bayes' Nets | Ch 14.1-2,4 | Homework 1 (p1) | 2/7 | |
| Tu 2/5 | Bayes' Nets | Ch 14.3 | 6PP | ||
| Th 2/7 | Bayes' Nets | Ch 14.4-5 | |||
| Tu 2/12 | Hidden Markov Models | Ch 15.1-3 | Notes Tutorial | Homework 2 (p2) | 2/19 in class |
| Th 2/14 | Hidden Markov Models | Ch 15.1-3 | |||
| Tu 2/19 | Hidden Markov Models, Speech Recognition | Ch 15.6 | HMM Programming Project (p3) | 2/26 11:59pm | |
| Th 2/21 | Speech Recognition, Utility | ||||
| Tu 2/26 Th 2/28 |
Utility and Decisions | Ch 16.1-3,5 Ch 17.1-3 |
Utility and Decisions (p4) | 3/6 in class | |
| Tu 3/4 | Markov Decision Processes, Reinforcement Learning | Reinforcement Learning Sutton and Barto Ch 3,4 | |||
| Th 3/6 | Value Iteration, Policy Iteration, Monte Carlo Policy Evaluation | Reinforcement Learning Ch 5, 6.1-5 |
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| Tu 3/11 | Reinforcement Learning, Classification | Ch 20.2 | Reinforcement Learning (p5) | 3/21 11:59pm | |
| Th 3/13 | Perceptrons, Neural Networks | Ch 20.5 | |||
| Tu 3/18 | |||||
| Th 3/20 | Midterm | ||||
| Tu 4/1 Th 4/3 |
Classification: Neural Networks & Backpropagation, Decision Trees, Nearest Neighbor | Ch 18.3 Ch 20.4-5 |
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| Tu 4/8 | Support Vector Machines | Ch 20.6 | Neural Networks (p6) | 4/17 11:59pm | |
| Th 4/10 | A* Search and Heuristics | Ch 3.3-4 Ch 4.1-2 |
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| Tu 4/15 | Mutiplayer Games, Alpha-Beta Pruning | Ch 6 Ch 17.6 |
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| Th 4/17 | Games, Nash equilibrium, Knowledge Representation, Logic | Ch 6 Ch 17.6 |
Notes: Page 1 Page 2 |
Homework 7 (p7) | 4/24 in class |
| Tu 4/22 | Vision | ||||
| Th 4/24 | Vision |
Slides |
Homework 8 (p8) | 5/8 11:59pm | |
| Tu 4/29 Th 5/1 |
Vision |
Slides |
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| Tu 5/6 Th 5/8 |
Language and Review | ||||
| M 5/19 | Final Exam |
