Statistical Learning Methods with R

Introduction to R (statistical software); statistical models and evaluation with R; regression simple and multiple; logistic regression; k-nearest neighbors; linear discriminant analysis (LDA); model evaluation, variable selection and regularization; resampling approaches & evaluation; support vector machines (SVM); boosting; unsupervised approaches (principal component analysis, multidimensional scaling);

The final mark is based on both a final written exam and the results of the practical exercises.


Code 32079
Type Course
Site Neuchâtel
Track(s) T3 – Advanced Information Processing
T6 – Data Science
Semester S2023


Learning Outcomes

The main objectives of this course is to introduce the students to the various techniques coming mainly for the statistical domain in the machine learning paradigm. The main objectives of the course are the followings:

  1. to be able to use and program in R, the statistical language (used to analyze Big Data);
  2. to select, apply and evaluate a learning method using R and to interpret the results;
  3. to select the most appropriate method according to the data, and to evaluate the quality of the fit.

Practical exercises will complete the theoretical presentation.

Lecturer(s) Jacques Savoy
Language english
Course Page

The course page in ILIAS can be found at

Schedules and Rooms

Period Weekly
Schedule Wednesday, 13:45 - 17:15
Location UniNE, Unimail
Room B104


Evaluation type written exam

Additional information


First Lecture
The first lecture will take place on Wednesday, 22.02.2023 at 13:45 in UniNE, Unimail, room B104.


  • G. Jones, D. Witten, T. Hasti, R. Tibshirani: An Introduction to Statistical Learning. With Applications in R. Springer, 2013.
  • C.M. Bishop: Pattern Recognition & Machine Learning. Springer, 2006.
  • M.J. Crawley: The R Book. 2nd Ed., Wiley, 2012.