CSE P546 Data Mining - Spring 2010

University of Washington Department of Computer Science & Engineering

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Contact Info

Instructor: Jesse Davis
Office: CSE 356
Office hours: Mondays 5:30-6:20, and by appointment TA: Andrey Kolobov
Office: CSE 216
Office hours: Mondays 5:30-6:20, and by appointment

Class meets:
Mondays 6:30-9:20 in EEB 037

Lectures

Week	Topics	Reading	Lecture Notes
Week 1 (Mar 29)	Introduction; Inductive learning, Instance-based learning	Mitchell, Chapters 1, 2 and 8.2	pdf, pptx
Week 2 (Apr 5)	Decision trees; Empirical evaluation	Mitchell, Chapters 3 and 5	pdf
Week 3 (Apr 12)	Bayesian Learning	Mitchell, Chapter 6	pdf
Week 4 (Apr 19)	Rule Induction	Mitchell, Chapter 10	pdf
Week 5 (Apr 26)	Neural networks	Mitchell, Chapter 4	pdf
Week 6 (May 3)	Genetic algorithms, model ensembles	Mitchell, Chapter 9	pdf
Week 7 (May 10)	SVMs; Learning theory	Mitchell, Chapter 7	pdf
Week 8 (May 17)	Clustering	Chapters 16 and 17	pdf
Week 9 (May 24)	Association rules	This	pdf
Week 10 (May 31)	Memorial Day - No Class

Assignments

There will be four to five homework assignments, all to be done individually. Assignments are due by the start of class. Each homework will be weighted equally when calculating the final grade. The late turn-in policy is: 10% penalty per day late, up to a maximum of one week.

Schedule	Assignment	# of Points	Topic
Due 6.30 p.m., April 12	#1	100	Collaborative Filtering, IBL, D-trees, Methodology
Due 6.30 p.m., April 26	#2	100	Rule Induction and Bayesian Learning
Due 6.30 p.m., May 10	#3	110	Voted Perceptron, Ensemble methods, Genetic Algorithms
Due 6.30 p.m., June 1st	#4	100	Learning theory, SVMs, clustering, association rules

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Textbooks

Tom Mitchell, Machine Learning, McGraw-Hill, 1997. (Required)
R. Duda, P. Hart & D. Stork, Pattern Classification (2nd ed.), Wiley, 2001 (recommended).

Papers

J. Breese, D. Heckerman and C. Kadie, Empirical Analysis of Predictive Algorithms for Collaborative Filtering, MSR Tech Report, 1998
F. Provost, T. Fawcett and R. Kohavi, The Case Against Accuracy Estimation, Proceedings of 15th International Conference on Machine Learning, 1998.
J. Davis and M. Goadrich (2006). The Relationship Between Precision-Recall and ROC Curves,Proceedings of the 23rd International Conference on Machine Learning, 2006
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.
G. John, Behind-the-scenes data mining, SIGKDD Explorations, 1999.

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