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

Twice a year

Location

Remotely on Zoom

PrerequisitesComment

• Basic knowledge of statistics
• Be confortable with either Python or R programming
• Have an account
  Accounts on the UNILDCSR cluster Curnagl.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

  Prerequisites

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

  IMPORTANT: Please register using your UNIL email address!

   

  Course dates and registration