# Statistical Learning

Elective econometric course for third-year Bachelor students

Coure Description on UvA’s website

## Topics

- optimal prediction rules;
- cross-validation and the bootstrap;
- model selection and regularization methods (ridge and lasso);
- linear regression and classification;
- nonlinear models, splines and generalized additive models;
- tree-based methods, random forests and boosting.

## R programming

- Introduction to R
- Cross validation
- Linear regression
- Subset Selection Methods
- Ridge and LASSO regression
- Principal components regression
- Logistic regression
- Linear and quadratic discriminant analysis
- Polynomial and spline regression
- Splines regression
- Fitting classification and regression trees
- Random forest

## Textbook

James, G. et al. (2013).
*An Introduction to Statistical Learning - with Applications in R.*