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Welcome to the 573 Home Page.
Instructor: Dan Weld
[ Administrivia | Topics & Schedule | Problem Sets | Mini-Project 1 | Mini Project 2 ]
The primary goal of this class is to provide a rigourous introduction to Artificial Intelligence, explaining the challenges inherent in building an intelligent system and describing the main techniques and tools. We will focus on search, knowledge representation and reasoning, planning and learning - dividing our time between techniques based on logic and on probabalitsic methods. A secondary goal of the course is to teach how to read and analyze research papers. Thus some of the reading will be in the textbook (or a survey paper) and some will be recent conference papers; in the latter case, students will write program-committee style reviews. There will be two mini-projects, several short problem sets, and a midterm.
Administrivia
Class schedule: M, W
Dan's office hours: Monday
TA Xu Miao's office
hours TBD; or email (xm at cs) for an appointment
- The class mailing list is (cse573 at cs.washington.edu). Here are instructions on joining and an archive.
- When I assign a research paper, I'll ask you to submit a mini-review. Here's how.
- Your suggestions can really improve the class, so please don't hesitate. Either find me in person, send me email, or use the Departmental anonymous feedback form
The (required) textbook is Russell & Norvig AI a Modern Approach, (Prentice Hall) 2nd edition, 2003. (Note: this book is much improved over the previous edition). We will also assign readings off the WWW.
Your final grade will be assigned based on the following (tentative weighting):
- 60% mini-projects
- 25% midterm (solution)
- 10% paper summaries
- 5% class participation
Schedule, Assignments & Slides
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Date |
Topic |
Reading due |
Lecture Slides |
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Sept 29 (W) |
Introduction, administrivia, agents |
AIMA ch 2 |
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Oct 4 (M) |
Problem spaces; search (blind, informed, local) |
AIMA Ch 3, 4 (except 4.2) |
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Oct 6 (W) |
Heuristic generation, constraint satisfaction |
AIMA Ch 4.2, Ch 5 |
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Oct 11 (M) |
Adversarial search |
AIMA Ch 6 |
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Oct 13 (W) |
Knowledge representation (propositional logic) |
AIMA Ch 7 |
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Oct 18 (M) |
Knowledge representation continued (first-order logic) |
AIMA Ch 8, Ch 9 thru p 278; Section 10.3 |
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Oct 20 (W) |
Planning |
AIMA Ch. 11 |
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Oct 25 (M) |
… continued |
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Oct 27 (W) |
Learning |
AIMA Sections 18.1-18.3 |
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Nov 1 (M) |
... continued |
AIMA through 18.4 |
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Nov 3 (W) |
Inference under uncertainty |
AIMA Ch. 13 thru 14.5, but skip p501-4 |
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Nov 8 (M) |
Probabilistic reasoning about time (HMMs, DBNs) |
AIMA Ch. 15 - 15.2 |
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Nov 10 (W) |
Markov decision processes (MDPs) |
AIMA Ch. 17-17.2 & 17.4 |
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Nov 15 (M) |
Reinforcement Learning |
AIMA Ch 21 |
14-reinf-learn (ppt) (pdf); Pole demo; Aibo walking demo (before) (after); |
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Nov 17 (W) |
Statistical learning |
AIMA Ch. 20-20.2 |
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Nov 22 (M) |
MIDTERM |
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Nov 24 (W) |
No class |
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Nov 29 (M) |
Naive Bayes & Expectation Maximization |
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Dec 1 (W) |
Artificial Life |
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Dec 6 (M) |
Probabalistic Cruciverbalism |
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Dec 8 (W) |
Intelligent Internet Systems |
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