1. Introduction and Overview (1 lecture)

2. Data Warehousing and OLAP: ( 1 lecture)

3. Data Pre-Processing, Cleaning, Reduction, Feature Extraction and Visualization: (3 lectures)

4. Classification : (4-5 lectures)

5. Mining Association Rules, Market Basket Analysis: ( 1 lecture)

6. Clustering/Segmentation: (5 lectures)

7. Prediction/Regression; Combining Multiple Models: ( 2 lectures)

8. Advanced Topics : (3 lectures)
Kernel methods; co-clustering etc.

9. Intro to Web Link Analysis : (1 lecture)
Google's Pagerank; Hubs and authorities.

Term Paper Presentations

10. Course wrap-up; outlook. : (1 lecture)