| Class | Date | Day | Topic |
| 1 | 8/25 | Thu | Statistics, Machine Learning, and Data Mining |
| 2 | 8/30 | Tue | Decision Trees: Recursive Partitioning, Splitting Rule |
| 3 | 9/01 | Thu | Decision Trees: Efficiency, Pruning, Converting Trees to Rules |
| 4 | 9/06 | Tue | Train/Test Set Splits |
| 5 | 9/08 | Thu | Artificial Neural Nets: Backpropagation |
| 6 | 9/13 | Tue | Artificial Neural Nets: Overfitting (Weight Decay, Early Stopping, and Network Size) |
| 7 | 9/15 | Thu | Artificial Neural Nets: Multitask Learning |
| 8 | 9/20 | Tue | Cross Validation |
| 9 | 9/22 | Thu | Feature Selection |
| 10 | 9/27 | Tue | Data Preprocessing: Attribute Transformation & Missing Values |
| 11 | 9/29 | Thu | K-Nearest Neighbor |
| 12 | 10/04 | Tue | Boosting & Bagging and the Bias/Variance Tradeoff |
| 13 | 10/06 | Thu | Boosting & Bagging |
| --- | 10/11 | Tue | No Class -- Fall Break |
| 14 | 10/13 | Thu | SVMs |
| 15 | 10/18 | Tue | SVMs |
| 16 | 10/20 | Thu | Performance Metrics: Accuracy, ROC, RMSE, ... |
| 17 | 10/25 | Tue | Empirical Comparison of Learning Methods |
| 18 | 10/27 | Thu | |
| 19 | 11/01 | Tue | Data Mining of Association Rules |
| 20 | 11/03 | Thu | Agglomerative Clustering |
| 21 | 11/08 | Tue | K-Means Clustering & EM (Expectation Maximization) |
| 22 | 11/10 | Thu | Scaling Clustering to Large Data Sets |
| 23 | 11/15 | Tue | Fractal Dimension |
| 24 | 11/17 | Thu | Multi-dimensional Scaling |
| 25 | 11/22 | Tue | |
| --- | 11/24 | Thu | No Class -- Thanksgiving |
| 26 | 11/29 | Tue | Case Study: Pneumonia Risk Prediction (Prospective Analysis) |
| 27 | 12/01 | Thu | Case Study: Protein Folding (Clustering, Visualization, & Discovery) |
| --- | 12/09 | Thu | Final Exam (open book) 9:00am-11:30am Place: TBA |