Statistical Learning


This course covers topics of statistical learning in a mathematical and economical approach.

Specific topics are

  • Linear Regression
  • Classification
  • Model assessment, selection and inference: cross-validation, bootstrap
  • Regularization methods: Ridge and Lasso regression
  • Regression Splines
  • Tree-Based Methods: 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
  • Gain or deepen, respectively, 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


Please write me at if you are interested in attending the course. I will keep you posted regarding the organization of the course in April.

Time and Venue

The course schedule is:

  • Lecture: Monday, 08:30-10:00, Online Video on Moodle
  • Exercise class: Wednesday, 14:15-15:45, Online Video on Moodle, biweekly


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


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