Date | Class Period & Lecture Topic | Reading | Assignment |
---|---|---|---|
Jan 06 | 1. Syllabus and Introduction | ||
Jan 08 | 2. Classification/Regression | Linear Regression Tutorial (S. Waner, Hofstra University), Simple Logistic Regression (R. Lowry, Vassar College) |
Wiki Assignment |
Jan 13 | 3. Clustering | Clustering (M.H. Dunham, Data Mining: Introductory and Advanced Topics, Pearson Education Inc., 2003, Chapter 5): Sections 5.1-5.5.5 | |
Jan 15 | 4. Correlation | Apriori (P-N. Tan, M. Steinbach and V. Kumar, Introduction to Data Mining, Pearson Education Inc., 2006, Chapter 6): Sections 6.1-6.3; some possibly helpful slides (C. Giraud-Carrier and D. Norton, Brigham Young University) | Linear Models |
Jan 20 | 5. Data-driven Model Construction | ||
Jan 22 | 6. The ML/DM Process | Data Mining Methodology: The Virtuous Cycle Revisited (M.J.A. Berry and G.S. Linoff, Mastering Data Mining, Wiley, 2000, Chapter 3) | Clustering |
Jan 27 | 7. No Lecture | Contribute a Group Project Idea -- See the Wiki | |
Jan 29 | 8. WEKA Overview | Machine Learning with WEKA (E. Frank, University of Waikato, New Zealand) | Apriori |
Feb 03 | 9. Intro to Group Project | ||
Feb 05 | 10. Feature selection (PCA) | A Tutorial on Principal Components Analysis (L. I. Smith, University of Otago, New Zealand) | WEKA exercise |
Feb 10 | 11. Error and other utility measures | Evaluation of models (parts 1 and 2) (Data Mining Tutorial, Rudjer Boskovic Institute), Confusion Matrix (H.J. Hamilton, Online Notes for CS 831, University of Regina, Canada), ROC Graph (H.J. Hamilton, Online Notes for CS 831, University of Regina, Canada) |
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Feb 12 | 12. Instance-based Learning | Instance-Based Learning (Tom M. Mitchell, Machine Learning, McGraw Hill, 1997, Chapter 8): Sections 8.1-8.4 | Report: Objectives, Requirements, Plan |
Feb 17 | 13. No Lecture (Monday Instruction) | ||
Feb 19 | 14. Review/Grad School | WEKA Lab 1 | |
Midterm (Feb 23–25 in the testing center) | |||
Feb 24 | 15. No Lecture (Midterm) | ||
Feb 26 | 16. Decision Trees | Decision Tree Learning (Simon Colton, Imperial College, London) | Report: Initial Data |
Mar 03 | 17. Multi-layer Perceptrons | Multi-Layer Artificial Neural Networks (Simon Colton, Imperial College, London) | |
Mar 05 | 18. Multi-layer Perceptrons | Report: Final Data | |
Mar 10 | 19. Support Vector Machines | Support Vector Machines (Marti Hearst, IEEE Intelligent Systems, pp. 18-28, July/August 1998 -- focus on first 3.5 pages) | |
Mar 12 | 20. Support Vector Machines | ||
Mar 17 | 21. Model Selection | Paired Permutation Test | Modeling |
Mar 19 | 22. Bias/Variance | ||
Mar 24 | 23. Ensembles | Experiments with a New Boosting Algorithm (Yoav Freund and Robert E. Shapire, Proceedings of the International Conference on Machine Learning, pp. 148-156,1996) | Report: Verification WEKA Lab 2 |
Mar 26 | 24. Association Mining | ||
Mar 31 | 25. Artificial Ethics? |
Brave New Era for Privacy Fight (Wired, 13 January 2005), In Age of Security, Firm Mines Wealth of Personal Data (Washington Post, 20 January 2005), ChoicePoint: We're Sorry for Data Leak (CNET News, 15 March 2005), Can Data Mining Catch Terrorists? (Information Week, 22 May 2006), U.S. Doctors Object to Data-Mining (Herald Tribune, 4 May 2006) |
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Apr 02 | 26. Creative Intelligent Systems | ||
Apr 07 | 27. Oral Presentations | Music Group, Robot Group, Road Group | |
Apr 09 | 28. Oral Presentations | Speech Group, Library Group, Quiz Group, Talks Group | |
Apr 14 | 29. Review | Group Project Report | |
Apr 16 | Reading Day | ||
Apr 17 | Final (7:00am–10:00am in class) |