CSE P546 Data Mining - Spring 2007

University of Washington Department of Computer Science & Engineering

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Contact Info

Instructor: Pedro Domingos
Office: CSE 648
Office hours: Wednesdays 5:30-6:20, and by appointment TA: Bhushan Mandhani
Office: CSE 216
Office hours: Wednesdays 5:30-6:20, and by appointment

Class meets:
Wednesdays 6:30-9:20 in EEB 045

Lectures

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

Assignments

There will be two projects and three homework assignments, all to be done individually. Assignments are due by the start of class. The late turn-in policy is: 10% penalty per day late, up to a maximum of one week.

Schedule	Assignment	% of Grade	Topic
Weeks 2-3	Homework 1	20	Data warehousing and OLAP, decision trees
Weeks 3-6	Project 1	20	Clickstream mining
Weeks 6-7	Homework 2	20	Rule ind., Bayesian learning, neural nets, GAs, ensembles
Weeks 7-9	Project 2	20	Collaborative Filtering
Weeks 9-10	Homework 3	20	IBL, theory, association rules

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Textbooks

Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques, 2nd Edition, Morgan Kaufmann Publishers, 2005. (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 3 of Han & Kamber 2nd Ed (or 1 and 2 of 1st Ed) 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 5 of Han & Kamber 2nd Ed (or 6 of 1st Ed)
Week 10: Chapter 7 of Han & Kamber 2nd Ed (or 8 of 1st Ed)

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