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CSE 473 - Introduction to Artificial Intelligence - Autumn 2011 Mon, Wed, Fri 9:30-10:20 in MGH 231 |
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Contact Info |
| Instructor:
Dan Weld
(weld at cs dot washington dot edu) Office hours: Wed 10:30-11:20 CSE 588 or by email |
TA:
Lydia Chilton
(hmslydia at cs dot washington dot edu) Office hours: Tuesday 3:30-4:30, CSE 006 computer cluster |
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TA: Yisong Song
(titanium at cs dot washington dot edu) Office hours: Thursday 3:30-4:30, CSE 006 computer cluster |
| Date | Topics & Lecture Notes | Readings |
|---|---|---|
| March 26 | Introduction, Agents | Optional: R&N, Ch. 1 & Ch. 2 |
| March 28 | Problem Spaces & Blind Search | R&N, Sections 3.1 to 3.4 |
| March 30 | Heuristic Search | R&N, Section 3.5 |
| April 2 | Heuristics and Pattern Databases | R&N, Section 3.6 |
| April 4 | Constraint Satisfaction | R&N, Section 6.1-6.3 |
| April 6 | Local (Stochastic) Search | R&N, Section 4.1 |
| April 9 | Constraint Satisfaction, Part II | R&N, Section 6.1-6.3 |
| April 11 | Adversary Search (Minimax) | R&N, Section 5.1-5,2 |
| April 13 | Adversary Search (Alpha-Beta) | R&N, Section 5.3-5.4 |
| April 16 | Adversary Search (Expetimax) | R&N, Section 5.5 |
| April 18 | KR: Propositional Logic | R&N, Chapter 7 thru Section 7.5 |
| April 20 | KR: DPLL, Walksat | R&N, Chapter 7 thru Section 7.6 |
| April 23 | First-Order Logic | R&N, Sections 8.2-8.3.2, 8.4; 9.1-9.3.2 |
| April 25 | Automated Planning | R&N, Chapter 10 through Section 10.2 |
| April 27 | Automated Planning | R&N, Sections 10.3-10.5 |
| April 30 | Markov Decision Processes | R&N, Chapter 17 through Section 17.1 |
| May 2 | Markov Decision Processes | R&N, Section 17.2 |
| May 4 | Reinforcement Learning | R&N, Chapter 21 thru 21.3 |
| May 7 | Reinforcement Learning 2 | R&N, Section 21.4 |
| May 9 | POMDPs | R&N, Section 17.4 |
| May 11 | Uncertainty, Markov Models | R&N, Chapter 13, Section 15.1 |
| May 14 | Hidden Markov Models | R&N, Section 15.2-15.3 |
| May 16 | Particle Filters | R&N, Section 15.5.3 |
| May 18 | Bayesian Networks - Semantics | R&N, Chapter 14 thru Section 14.3 |
| May 23 | Bayesian Networks - Inference | R&N, Sections 14.4 and 14.4 |
| May 25 | Bayesian Networks - Parameter Learning | R&N, Chapter 18 thru 18.2; Chapter 20 thru 20.2.1; Section 20.2.4 |
| May 30 | Bayesian Networks - Hyrbid Networks, Naive Bayes, and Structure Learning | R&N, Section 20.2 |
| June 1 | Summary & Expectation Maximization | R&N, Section 20.3 |
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Department of Computer Science & Engineering University of Washington Box 352350 Seattle, WA 98195-2350 (206) 543-1695 voice, (206) 543-2969 FAX |