Course topics
Spring 2027. Topics and schedule are subject to change.
| Date | Topic | Slides | Other materials |
|---|---|---|---|
| Prediction tasks and baseline models | |||
| Data preparation and reproducibility | |||
| Ridge, lasso, and elastic net | |||
| Validation and leakage | |||
| Binary outcomes and evaluation metrics | |||
| Multiclass models and project questions | |||
| Feature engineering | |||
| Hyperparameter tuning | |||
| Support vector machines | |||
| SVM tuning and model comparison | |||
| Decision trees | |||
| Random forests and validation plans | |||
| Gradient boosting | |||
| Educational ensemble case study | |||
| Grouped and temporal evaluation | |||
| Interpretation and subgroup performance | |||
| Calibration and decision thresholds | |||
| Project modeling workshop | |||
| Mar 13 to 21 | Spring break, no class | ||
| Neural network architecture | |||
| Optimization and backpropagation | |||
| Neural network regularization | |||
| Neural network applications | |||
| Conformal prediction | |||
| Coverage, limits, and project findings | |||
| Reproducible research workflows | |||
| Paper workshop | |||
| Project presentations I | |||
| Project presentations II and synthesis |