| Class | Date | Day | Topic |
| 1 | 8/30 | Thu | Statistics, Machine Learning, and Data Mining |
| 2 | 9/04 | Tue | Decision Trees: Recursive Partitioning, Splitting Rule |
| 3 | 9/06 | Thu | Decision Trees: Efficiency, Pruning, Converting Trees to Rules |
| 4 | 9/11 | Tue | K-Nearest Neighbor |
| 5 | 9/13 | Thu | Artificial Neural Nets: Backpropagation |
| 6 | 9/18 | Tue | Artificial Neural Nets: Overfitting (Weight Decay & Early Stopping) |
| 7 | 9/20 | Thu | Artificial Neural Nets: Multitask Learning |
| 8 | 9/25 | Tue | Cross Validation |
| 9 | 9/27 | Thu | Feature Selection |
| 10 | 10/02 | Tue | Data Preprocessing: Attribute Transformation & Missing Values |
| 11 | 10/04 | Thu | Boosting & Bagging |
| --- | 10/09 | Tue | No Class -- Fall Break |
| 12 | 10/11 | Thu | Multi-dimensional Scaling |
| 13 | 10/16 | Tue | Clustering: Agglomerative |
| 14 | 10/18 | Thu | Clustering: EM (Expectation Maximization) & k-means Midterm Exam Handed Out (take home) |
| 15 | 10/23 | Tue | Fractal Dimension |
| 16 | 10/25 | Thu | Data Mining and Association Rules Midterm Exam Due Back |
| 17 | 10/30 | Tue | Computational Efficiency: KD-Trees and AD-Trees |
| 18 | 11/01 | Thu | Support Vectors: Maximum Margin |
| 19 | 11/06 | Tue | Support Vectors: Computational Efficiency |
| 20 | 11/08 | Thu | VC-dimension and PAC-learning |
| 21 | 11/13 | Tue | Learning from Text: Bags of Words Model |
| 22 | 11/15 | Thu | Time Series: Roll-Forward Cross Validation, Recurrent Neural Nets |
| 23 | 11/20 | Tue | Learning from Vision: Face Recognition & Autonomous Vehicle Navigation (ALVINN) |
| --- | 11/22 | Thu | No Class -- Thanksgiving |
| 24 | 11/27 | Tue | Case Study: Pneumonia Risk Prediction (Prospective Analysis) |
| 25 | 11/29 | Thu | Case Study: Ceasarean Section Prediction (Retrospective Analysis) |
| 26 | 12/04 | Tue | Case Study: Protein Folding (Clustering, Visualization, & Discovery) |
| 27 | 12/06 | Thu | Wrap-up |
| --- | 12/13 | Thu | 12:00-14:30 Final Exam (open book) |