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