Lecture Winter Term 2018/2019

Statistical Learning



Imma Curato

Class Teacher:

Dirk Brandes

MSc. Math, MSc. WiMa, MSc. Finance - elective course


The exercise class takes place every two weeks.

Time and Venue:The course schedule is:
  • Lecture: Monday, 16:00-18:00, He18 - 1.20
  • First Lecture: 15/10/2018
  • Exercise classThursday, 08:00-10:00, He18 - 1.20, biweekly
  • First Exercise class: 08/11/2018

Final Exam:

oral exam of 20 minutes.

To participate in the oral exam, you have to register at campusonline.uni-ulm.de


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

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.


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
  • Overview of non-linear models: splines, support vector machines and neural networks


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.

Exercise Sheets:


Lecture Notes: