Program Overview

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

Overview

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

The key competencies needed to apply decision tree and random forest methods to simple datasets will be acquired.

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

The course is 1 half-day long.

Organization

The course is held once per year.

Location

The course is held in-person only, with no online option.

Practicals

The practicals can be done on:

• The UNIL JupyterLab (available exclusively during this course and for one week following its completion)
• Your laptop (but you will need to install the required libraries)
• The UNIL cluster called Curnagl
• Note that in all cases you need to bring your own laptop

Prerequisites

• Basic knowledge of statistics
• Be comfortable with either Python or R programming
• To do the practicals:
  • On UNIL JupyterLab: Access requires that you connect either via the eduroam Wi-Fi with your UNIL account or through the UNIL VPN
  • On your laptop: No account requirement
  • On Curnagl: Please register using your UNIL email address