| LEC # | Topics | Key Dates |
|---|---|---|
| Introduction | ||
| 1 | Data Mining Overview, Prediction and Classification with k-Nearest Neighbors | |
| Classification | ||
| 2 | Classification and Bayes Rule, Naïve Bayes | |
| 3 | Classification Trees | Homework 1 out |
| 4 | Discriminant Analysis | |
| 5 | Logistic Regression Case: Handlooms | |
| 6 | Neural Nets | |
| 7 | Cases: Direct Marketing/German Credit | Homework 1 due Homework 2 out |
| Prediction | ||
| 8 | Assessing Prediction Performance | |
| 9 | Subset Selection in Regression | |
| 10 | Regression Trees, Case: IBM/GM weekly returns | Homework 2 due |
| Clustering | ||
| 11 | k-Means Clustering, Hierarchical Clustering | |
| 12 | Case: Retail Merchandising | |
| 13 | Midterm Exam | |
| Dimension Reduction | ||
| 14 | Principal Components | |
| 15 | Guest Lecture by Dr. Ira Haimowitz: Data Mining and CRM at Pfizer | |
| Data Base Methods | ||
| 16 | Association Rules (Market Basket Analysis) | |
| 17 | Recommendation Systems: Collaborative Filtering | |
| Wrap Up | ||
| 18 | Guest Lecture by Dr. John Elder IV, Elder Research: The Practice of Data Mining | |
| 19 | Project Presentations | |
