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