Standard syllabus
Machine learning for statistics · Graduate · Statistics
Topics
Statistical learning foundations
- Loss functions and empirical risk minimization
- Bias-variance and generalization error
- VC dimension and complexity control (intro)
- Regularization paths and model selection
- Cross-validation theory and practice
Modern methods
- Ensemble learning: boosting and bagging
- Kernel methods and SVMs
- Neural networks from a statistical view
- Unsupervised learning: clustering and embeddings
- Feature selection and sparsity
Evaluation and deployment
- Proper scoring rules
- Calibration and fairness metrics
- Interpretability: SHAP and LIME (overview)
- Statistical inference after model selection (intro)
Pricing
Graduate-level rates are set on consultation. See the pricing page for K–12 and undergraduate rates.