CSEP 546 - Data Mining

University of Washington Department of Computer Science & Engineering

CSEP 546 - Data Mining - Autumn 2004

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Contact Info

Instructor: Pedro Domingos
Office: CSE 648
Office hours: Tuesdays 5:30-6:20, and by appointment TA: Parag
Office: CSE 216
Office hours: Tuesdays 5:30-6:20, and by appointment

Class meets:
Tuesdays 6:30-9:20 in EE1 037

Lectures

Week	Topics & Lecture Notes
Week 1 (Oct 5)	Introduction; data warehousing and OLAP PPT , PDF
Week 2 (Oct 12)	Inductive learning, decision trees PPT , PDF
Week 3 (Oct 19)	Rule induction PPT, PDF
Week 4 (Oct 26)	Bayesian learning PPT, PDF
Week 5 (Nov 2)	Neural networks PPT, PDF
Week 6 (Nov 9)	Genetic algorithms, model ensembles PPT, PDF
Week 7 (Nov 16)	Instance-based learning PPT, PDF
Week 8 (Nov 23)	Learning theory PPT, PDF
Week 9 (Nov 30)	Association rules PPT, PDF
Week 10 (Dec 7)	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	Text classification
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, 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

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