Steam-powered Turing Machine University of Washington Department of Computer Science & Engineering
 CSE 592 - Applications of AI: Data Mining- Spring 2001
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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


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


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 Additional Info
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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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

CSE logo Department of Computer Science & Engineering
University of Washington
Box 352350
Seattle, WA  98195-2350
(206) 543-1695 voice, (206) 543-2969 FAX
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