Statistical Learning


This course covers the follwing topics in statistical learning:

  • Linear Regression
  • Classification
  • Model assessment, selection and inference: cross-validation, bootstrap
  • Regularization methods: Ridge and Lasso regression
  • Regression Splines
  • Tree-Based Methods
  • Bagging, Random Forests and Boosting

Learning Objective

By attending the course you will 

  • understand and master fundamental principles and modelling techniques for the analysis of regression and classification problems
  • aquiring model assessment and inference techniques for linear and non-linear models.
  • exercising the acquired techniques by means of real data sets and the R software.

Lecture Notes and Exercises

All materials will be available on Moodle.


The course follows the following books:

  • T. Hastie, R. Tibshirani & J. Friedman, The Elements of Statistical Learning: data mining, inference and prediction, 2nd edition, Springer, 2009.
  • G. James, D. Witten, T. Hastie & R. Tibshirani, An Introduction to Statistical Learning with Applications in R, Springer, 2013.
  • W.H. Green, Econometric Analysis (Seventh Edition), Pearson, 2012.
  • D.W. Hosmer, S. Lemeshow, R.X. Sturdivant, Applied Logistic Regression (Third Edition), 2013.
  • G. Casella, R.L. Berger, Statistical Inference (Second Edition), 2001.
  • B. Efron and R.J. Tibshirami, An Introduction to the Bootstrap, Chapman & HALL/CRC, 1994.


Imma Curato


Write at if you have any question about the course.

More details about the organization of the course in the summer semester will follow in April.

Time and Venue

The course schedule is:

  • Lecture: 
  • Exercise class:


MSc. Math, MSc. WiMa, MSc. Finance - elective course (4 Credit Points)


Analysis I+II, Elementary Statistics and Probability, Stochastic I, and Measure Theory