CSE 592 - Applications of Artificial Intelligence

University of Washington Department of Computer Science & Engineering

CSE 592 - Applications of AI: Data Mining- Spring 2001

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Contact Info

Instructor: Pedro Domingos
Office: Sieg 216
Office hours: Wednesdays 5:30-6:20, and by appointment TA: David Grimes
Office: Sieg 226a
Office hours: Wednesdays 5:30-6:20, and by appointment

Class meets:
Wednesdays 6:30-9:20 in EE1-003

Topics

Week	Topic
Week 1 (Mar 28)	Introduction; data warehousing and OLAP
Week 2 (Apr 4)	Decision trees
Week 3 (Apr 11)	Rule induction
Week 4 (Apr 18)	Bayesian learning
Week 5 (Apr 25)	Neural networks
Week 6 (May 2)	Genetic algorithms, model ensembles
Week 7 (May 9)	Instance-based learning
Week 8 (May 16)	Learning theory
Week 9 (May 23)	Association rules
Week 10 (May 30)	Clustering

Assignments

There will be two projects and three homeworks assignments. You are expected to work individually on the homeworks, and in groups of two on the projects.

Schedule	Assignment	% of Grade	Topic
Weeks 2-3	Homework 1	12.5	Warehousing and OLAP, decision trees
Weeks 3-6	Project 1	37.5	Clickstream mining
Weeks 6-7	Homework 2	12.5	Rule ind., Bayesian learning, neural nets, GAs
Weeks 7-9	Project 2	25	Collaborative filtering
Weeks 9-10	Homework 3	12.5	Ensembles, IBL, theory, assoc. rules

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Textbooks

Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers, 2000. (Required)
Tom Mitchell, Machine Learning, McGraw-Hill, 1997. (Required)

Papers

G. John, Behind-the-scenes data mining, SIGKDD Explorations, 1999.
T. Dietterich, Machine-learning research: Four current directions, AI Magazine, 1997.
P. Domingos, A unified bias-variance decomposition, Seventeenth National Conference on Artificial Intelligence, 2000.

Readings

Week 1: Chapters 1 and 2 of Han & Kamber and Behind-the-scenes data mining
Week 2: Chapter 3 of Mitchell
Week 3: Chapter 10 of Mitchell; review first-order logic
Week 4: Chapter 6 of Mitchell; review probability and statistics
Week 5: Chapter 4 of Mitchell; review calculus
Week 6: Chapter 9 of Mitchell and Section 2 of Machine-learning research: Four current directions
Week 7: Chapter 8 of Mitchell
Week 8: Chapter 7 of Mitchell and A unified bias-variance decomposition
Week 9: Chapter 6 of Han & Kamber
Week 10: Chapter 8 of Han & Kamber

Lecture Notes

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