| Lecture 1 | Course Introduction | |
| Lecture 2 | Data Pre-Processing | HW 1 Out |
| Lecture 3 | Data Exploration and Visualization | |
| Lecture 4 | Decision Trees | HW 2 Out |
| Lecture 5 | Model Evaluation | |
| Lecture 6 | Rule-Based and Bayesian Classification | HW 3 Out |
| Lecture 7 | Non-linear Classification | |
Mid-Term Exam |
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| Lecture 8 | Ensemble Methods | |
| Lecture 9 | Regression Analysis | HW 4 Out |
| Lecture 10 | Clustering Algorithms | |
| Lecture 11 | Clustering Algorithms (Contd.) | |
| Lecture 12 | Association Analysis | HW 5 Out |
Thanksgiving Break |
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| Lecture 13 | Anomaly Detection and DM Applications | |
| Lecture 14 | Top10 Algorithms and Course Review | |
Project Presentations |
Project Report Due | |
| Final Exam |