CSE 473 - Introduction to Artificial Intelligence

University of Washington Department of Computer Science & Engineering

CSE 473 - Introduction to Artificial Intelligence - Autumn 2000

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About Us

Contact Info

Instructor: Pedro Domingos
Office: Sieg 216
Office hours: Mondays 3:30 - 4:30, and by appointment TA: David Azari
Office: Sieg 226a
Office hours: Tuesdays 1:30 - 2:30, Thursdays 2:30 - 3:30

Class meets:
MWF 1:30-2:20 EE1 045

Topics

Problem solving and search
Representation and reasoning
Uncertainty
Machine learning
Planning

Assignments

There will be four homeworks, two projects, and a final exam. You are expected to work individually on the homeworks, and in groups of two on the projects.

Schedule	Assignment	% of Grade	Topic	Additional Info
Weeks 2-3	Homework 1 (solutions)	5	Search
Weeks 3-6	Project 1	20	Satisfiability solver
Weeks 4-5	Homework 2 (solutions)	5	Reasoning
Weeks 6-7	Homework 3 (solutions)	5	Uncertainty
Weeks 8-9	Homework 4 (solutions)	5	Learning	Problem#1
Weeks 8 - 10.5	Project 2	20	Ensemble learner
	Final: (Word file), (Postscript)	40	Comprehensive	Due Dec. 11, 12pm

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Textbooks

Required: Stuart Russell & Peter Norvig, Artificial Intelligence: A Modern Approach, Prentice-Hall, 1995.
Recommended: Tom Mitchell, Machine Learning, McGraw-Hill, 1997.

Papers

D. Weld, Recent advances in AI planning, AI Magazine, 1999.
T. Dietterich, Machine-learning research: Four current directions, AI Magazine, 1997.

Readings

Week 1: Chapters 1 and 3 of Russell & Norvig
Week 2: Chapter 4 of Russell & Norvig and SAT solvers section of Weld paper
Week 3: Chapters 5 and 6 of Russell & Norvig
Week 4: Chapters 7 and 9 of Russell & Norvig
Weeks 5 & 6: Chapters 14, 15 and 16 of Russell & Norvig; review probability and statistics
Week 7: Chapters 1, 2 and 3 of Mitchell
Week 8: Ensembles section of Dietterich paper and Chapter 8 of Mitchell
Week 9: Chapters 6 and 4 of Mitchell
Week 10: Sections 1.1, 2.1 and 2.2 of Weld paper

Lecture Notes

Week 1: Introduction, uninformed search
Week 2: Informed search, constraint satisfaction, satisfiability
Week 3: Game playing, propositional logic
Week 4: First-order logic (Part 1, Part 2, Part 3)
Weeks 5 & 6: Uncertainty (Part 1, Part 2, Part 3, Part 4, Part 5)
Week 7: Intro to machine learning, version spaces, decision trees (Part 1, Part 2)
Week 8: Learning ensembles, instance-based learning
Week 9: Bayesian learning (Part 1, Part 2), neural networks (Part 1, Part 2)
Week 10: Planning, review

Mail Archive

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