Lecture notes will be linked to this web page, in PDF format. The reader for PDF files is available free from Adobe for UNIX, Apple Macintosh, and Windows.
| Introduction | RN, Chapter 1 & 2 | Lecture slides | |||
| Uninformed and Informed Search | RN, Chapter 3 | Lecture slides | |||
| Search for optimization problems | RN, Section 4.1 | Lecture slides | |||
| Searching under uncertainty | RN, Sec. 4.2-4.5 | Lecture slides |
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| Game playing | RN, Chapter 5 | Lecture slides | |||
| Constraint satisfaction | RN, Chapter 6 | Lecture slides |
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| Knowledge Representation & Logic | RN, Chapters 7, 8, 9 | Lecture slides | |||
| Classical Planning | RN, Chapter 10 | Lecture slides |
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| Introduction to reasoning under uncertainty: Probabilities |
RN, Chapter 13 | Lecture slides | |||
| Midterm Exam |
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Held in Trottier 0100 |
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| Bayesian Networks |
RN, Sec. 14.1, 14.2, 14.4 |
Lecture slides | |||
| Bayesian Networks (cont'd) | RN, Sec. 14.1, 14.2, 14.4 | Lecture slides |
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| Study week. | |
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| Study week. | |
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| Maximum likelihood | RN, Sec. 20.1-20.2 (up to p.719) | Lecture slides | |||
| Expectation maximization | RN, Sec. 20.3 | Lecture slides |
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| Supervised learning: Linear predictors, gradient descent | RN, Sec.18.1, 18.2, 18.4, 18.6 | Lecture slides | |||
| Decision Trees | RN, Sec. 18.3 | Lecture slides |
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| Neural networks: Sigmoid neurons, backpropagation | RN, Sec. 18.7 | Lecture slides |
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| Feature Selection | | Lecture slides |
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| Utility theory | RN, Chapter 16 | Lecture slides | |||
| Markov Decision Processes | RN, Sec. 17.1 - 17.3 | Lecture slides |
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| Reinforcement learning | RN, Sec. 21.1, 21.2, 21.4 | Lecture slides | |||
| Applications in medicine | |
Lecture slides |
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| Hidden Markov Models | RN, Sec. 15.1-15.3 | Lecture slides | |||
| Natural language processing | | Lecture slides |
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| Easter Monday |
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| Robotics | RN, Chapter 25 | Lecture slides | |||
| AI: Present and Future. | RN, Chapter 26 and 27 | Lecture slides |