Machine learning for statistics
Graduate · Math
Syllabus focus
Topics typically covered
Click a topic for the full text and related unit practice.
Graduate Machine learning for statistics — outline derived from course README sections and typical US statistics syllabi (OpenIntro / standard OER where applicable).
Statistical learning foundations
- Loss functions — Loss functions
- Empirical risk minimization — Empirical risk minimization
- Bias-variance — Bias-variance
- Generalization error — Generalization error
- VC dimension — VC dimension
- Complexity control (intro) — Complexity control (intro)
- Regularization paths — Regularization paths
- Model selection — Model selection
- Cross-validation theory — Cross-validation theory
- Practice — Practice
Modern methods
- Ensemble learning: boosting — Ensemble learning: boosting
- Bagging — Bagging
- Kernel methods and SVMs — Kernel methods and SVMs
- Neural networks from a statistical view — Neural networks from a statistical view
- Unsupervised learning: clustering — Unsupervised learning: clustering
- Embeddings — Embeddings
- Feature selection and sparsity — Feature selection and sparsity
Evaluation and deployment
- Proper scoring rules — Proper scoring rules
- Calibration and fairness metrics — Calibration and fairness metrics
- Interpretability: SHAP — Interpretability: SHAP
- LIME (overview) — LIME (overview)
- Statistical inference after model selection (intro) — Statistical inference after model selection (intro)
Study units
Each unit includes a study guide, worksheets, review, practice test, and answer key. One unit is free; subscribe for the full class.
- Statistical learning foundations
Loss functions
Coming soon - Modern methods
Ensemble learning: boosting
Coming soon - Evaluation and deployment
Proper scoring rules
Coming soon
Notes
Graduate Machine learning for statistics — outline derived from course README sections and typical US statistics syllabi (OpenIntro / standard OER where applicable). Topic outline: `content/topics/graduate/ml_stats.json`.