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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.

Objectives

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

Content

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

Length

1 half-day

Organization

Once per year

Location

In presential

Practicals

The practicals can be done on the UNIL JupyterLab (available only for this course), on your laptop (but you will need to install the required libraries), or on the UNIL cluster called Curnagl. See the installation page for more information.

Prerequisites

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

IMPORTANT: To do the practicals
- On UNIL JupyterLab: You need to be able to access the eduroam wifi with your UNIL account or via the UNIL VPN
- On your laptop: No account requirement
- On Curnagl: Please register using your UNIL email address
- Note that in all cases you need to bring your own laptop


Course dates and registration