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Research - A Gentle Introduction to Decision Trees and Random Forests with Python and R

Machine learning methods are nowadays used in a wide variety of applications. In this course, you will learn how the decision tree and random forest methods work and may be applied in practice by using either Python or R programming.


Acquire the key competencies that are needed to apply decision tree and random forest methods to simple datasets

Target audience

Any PhD students, post-docs, researchers of UNIL who would like to use decision tree and random forest methods in their research


At the end of the course, the participants are expected to:

  • Understand how the decision tree and random forest algorithms work
  • Run simple machine learning codes in Python or R
  • Be able to choose properly the hyper-parameters of the models


1 half-day


Twice a year


Remotely on Zoom


Accounts on the DCSR cluster in a training project will be automatically created. You may try to work on your laptop, but we may not be able to help you with the installation


  • Basic knowledge of statistics
  • Be confortable with either Python or R programming

IMPORTANT: Please register using your UNIL email address!


Course dates and registration