Introduction to machine learning with R

Fribourg Pérolles,  Friday May 13, 2022,  9:00 – 17:00

(if legally possible in presence-mode, in case of lockdown in distance-mode)

This training is designed for anyone who wants to understand the fondamentals of machine learning.  It is given in English by Prof. Martin Huber.

This lecture provides an introduction to machine learning based on the software “R”,  however, only elementary basic working knowledge  of R is required.

Machine learning aims at predicting the value of an outcome of interest, e.g. sales or turnover, based on observing specific patterns of potentially relevant factors (or “predictors”) like price, quality, weather, advertisement campaigns etc. Importantly, such statistical methods allow learning from patterns among predictors in (past) data to forecast the value of the outcome in the future. This lecture first discusses the intuition and usefulness of machine learning for forecasting and taking actions (e.g. changing the price). It then introduces various statistical approaches such as regression and tree-based methods. Using the statistical software “R” and its interface “R Studio”, these methods are applied to various real-world data sets.


  • To understand the idea and goals of machine learning
  • To understand the intuition, advantages, and disadvantages of alternative statistical methods
  • To be able to apply machine learning methods to real-world data using the software “R” and its interface “R Studio”  (see also our introductory course in R)


  • Introduction to the concept and purpose of machine learning
  • Linear and non-linear regression (OLS, logit regression)
  • Penalized regression for variable selection and shrinking (lasso and ridge regression)
  • Tree-based approaches (trees, bagging, random forests)
  • Model tuning (cross-validation)
  • Performance evaluation (out-of-sample testing)
  • Application of all methods using the training
  • Software “R” and its interface “R Studio”

Course material:

Lecture slides and R code for applications to data

The lecture slides are based on “An Introduction to Statistical Learning with Applications in R” by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani (Springer, New York, 2013). The text book is available as pdf at http://www-bcf.usc.edu/~gareth/ISL/.

Further information:

Basic knowledge of R is expected. For that purpose, you can take advantage of  our introductory course in R  on Feb 12, 2021.

Participants are requested to bring their own laptop. (Please contact us if this is not feasible.)

The maximum number of participants is 18.

The participation fee is 500 CHF / 400 CHF for Swiss Engineering-section Fribourg members.



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