Syllabus
- Introduction
- The Nearest Neighbour algorithm
- Tree predictors
- Statistical learning
- Risk analysis for tree predictors
- Hyperparameter tuning and risk estimates
- Consistency and nonparametric algorithms
- Risk analysis for Nearest Neighbour
- Linear predictors
- Online gradient descent
- Kernel functions
- Support Vector Machines
- Stability and risk control for SVM
- Neural networks and deep learning
- Logistic regression and surrogate loss functions
- Boosting and ensemble methods