Statistical Learning

Statistical Learning

Elective econometric course for third-year Bachelor students

Coure Description on UvA’s website


  • 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


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

Textbook PDF