CSE 546 - Data Mining

University of Washington Department of Computer Science & Engineering

CSE 546 - Data Mining- Autumn 2001

CSE Home

About Us

Contact Info

Instructor: pedrod at cs.washington.edu
Office: Sieg 216
Office hours: Mondays 3:30-4:30 and by appointment TA: ghulten at cs.washington.edu
Office: Sieg 226a
Office hours: Thursdays from 12-1

Class meets:
Tuesdays and Thursdays 10:30-11:50 in Savery 153

Topics

The data mining process
Decision trees
Rule induction
Instance-based learning
Bayesian learning
Neural networks
Genetic algorithms
Model ensembles
Learning theory
Support vector machines
Clustering
Association rules

Project

Class evaluation will be by means of a project. Projects can be proposed by the students - for example, applying data mining techniques to your area of interest - or chosen from this list:

Scaling up any of the above methods by optimal subsampling
Developing language primitives for data mining
Mining social networks from the Internet
Predicting the evolution of the World Wide Web
Learning to automatically translate between representations
Personalizing Web sites via Web log mining
Mining multi-relational databases
Mining semi-structured data
Learning across different subpopulations
Learning given a query distribution
Modeling the hidden Web through querying
Incorporating prior knowledge into data mining algorithms

Projects can be carried in groups of two or individually; we encourage working in groups. In addition to a written report, students will give a short oral presentation of their work.

Textbooks

Tom Mitchell, Machine Learning, McGraw-Hill, 1997 (required).
David Hand, Heikki Mannila and Padhraic Smyth, Principles of Data Mining, MIT Press, 2001 (recommended).

Papers

C. Burges, A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery, 1998.
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.
P. Domingos & G. Hulten, Mining high-speed data streams [PDF], Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2000.
G. John, Behind-the-scenes data mining, SIGKDD Explorations, 1999.

Readings

Week 1: Chapter 1 of Hand and Behind-the-scenes data mining
Week 2: Chapter 3 of Mitchell
Week 3: Mining high-speed data streams [PDF]
Week 4: Chapter 10 of Mitchell; review first-order logic
Week 5: Chapter 8 of Mitchell
Week 6: Chapter 6 of Mitchell; review probability and statistics
Week 7: Chapter 4 of Mitchell; review calculus
Week 8: Section 2 of Machine-learning research: Four current directions
Week 9: Chapter 7 of Mitchell, A unified bias-variance decomposition, A tutorial on support vector machines, and Chapter 9 of Hand

Lecture Notes

Week 1: Introduction, inductive learning
Week 2: Decision trees
Week 3: Scaling up decision trees
Week 4: Rule induction
Week 5: Instance-based learning
Week 6: Bayesian learning
Week 7: Neural networks
Week 8: Model ensembles
Week 9: Learning theory and SVMs, clustering

Software

UWML, the UW Data Mining Lab, is a set of tools that you may find useful for your project. UWML is a work in progress; if you're planning to use it, please contact ghulten at cs.washington.edu. Pointers to various pieces of data mining software can be found at KDnuggets.

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[comments to Pedro Domingos]