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 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-person only (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), 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: Access requires that you connect either via the eduroam Wi-Fi with your UNIL account or through the UNIL VPN. This point is especially crucial for researchers from the CHUV. - 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