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
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