LEC # Topics Key Dates
1 Data Mining Overview, Prediction and Classification with k-Nearest Neighbors
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
8 Assessing Prediction Performance
9 Subset Selection in Regression
10 Regression Trees, Case: IBM/GM weekly returns Homework 2 due
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