Statistical Methods for Machine Learning

Syllabus

1. Introduction
2. The Nearest Neighbour algorithm
3. Tree predictors
4. Statistical learning
5. Risk analysis for tree predictors
6. Hyperparameter tuning and risk estimates
7. Consistency and nonparametric algorithms
8. Risk analysis for Nearest Neighbour
9. Linear predictors
10. Online gradient descent
11. Kernel functions
12. Support Vector Machines
13. Stability and risk control for SVM
14. Neural networks and deep learning
15. Logistic regression and surrogate loss functions
16. Boosting and ensemble methods