CSE 573 - Artificial Intelligence - Fall 1999

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Instructor: Pedro Domingos
Office: 216
Office hours: Monday after class or by appointment

Teaching Assistant: Adam Carlson
Office: 226b
Office hours: Wednesday after class or by appointment
You can visit me at my office hours, make an appointment, send me email or send mail to the class mailing list, which will be available from the course web.

Text: Thomas Dean, James Allen & Yiannis Aloimonos, Artificial Intelligence: Theory and Practice, plus papers to be assigned

Also recommended: Stuart Russell & Peter Norvig, Artificial Intelligence: A Modern Approach

Prerequisite: CSE graduate students only. Considerable computer science sophistication expected, but no prior knowledge of AI is necessary

Lecture notes:
Pedro's orientation slides (includes rough syllabus)
9/27: Uninformed search
9/29: Review of uninformed search, informed search
10/1: Genetic algorithms, game playing
10/4, 10/6: Introduction to AI
10/8, 10/11: Propositional logic
10/13: Constraint satisfaction
10/15: Satisfiability solvers
10/18: Predicate calculus (1, 2, 3)
10/20: Other representation & reasoning methods
10/22 to 11/1: Uncertainty (1, 2, 3, 4, 5)
11/3 to 11/22: Machine learning ( intro, version spaces, decision trees (1, 2), learning ensembles, rule induction, Bayesian learning (1, 2), nearest neighbor, neural nets (1, 2), theory (1, 2))
11/24, 11/29, 12/1: Planning
12/3, 12/6: Natural language processing, statistical natural language processing
12/8: Review

Readings:
Week 1: Chapter 4 (Search)
Weeks 2 & 3: Chapter 1 (Introduction), Chapter 3 (Representation and Logic) & SAT Solvers section of Weld paper (also in pdf)
Weeks 4 & 5: Chapter 8 (Uncertainty), and review probability & statistics
Weeks 6, 7 & 8: Chapter 5 (Learning) and Dietterich paper
Week 9: Chapter 7 (Planning) and Weld paper (also in pdf)
Week 10: Chapter 10 (Natural language processing) and 1997 AI Magazine article by Eugene Charniak and Webpage for Foundations of SNLP by Christopher Manning and Hinrich Schütze. (This web page has a couple of sample chapters. Of particular interest is the chapter on Markov Models. There's also a link to a resources page, which has many sources for corpora, tools and code.) A more general source is The Association for Computational Linguistics web page. This includes a search engine for computational linguistics websites. There's also a search engine for NLP papers.

Here is some advice about writing a computer science paper.

Assignments:
Project1
Project2

Final exam:
The final exam is now available. You can download it from here or pick up a copy from the fourth floor of Sieg, near the microwave. It is due Wed., Dec 15 at 12:30. postscript or pdf
The final exam solution is now available in postscript or pdf

	Department of Computer Science & Engineering University of Washington Box 352350 Seattle, WA  98195-2350 (206) 543-1695 voice, (206) 543-2969 FAX [comments to carlson@cs.washington.edu]