Credits: 1.0

Introduction to Machine Learning tools for Analysis in Life Sciences

20-21 October 2025

Neuchâtel

Instructor: Dr. Emmanuel Defossez

Description

This two-day course introduces participants to supervised machine learning methods applied to biological data using R software (+ VScode).

The first day includes an overview of the possibilities offered by machine learning, followed by hands-on practice: data preparation (cleaning, normalization, splitting into training and test sets), and training of various classification algorithms such as k-NN, logistic regression, LASSO, random forest, SVM or XGBoost... The course also covers feature selection (Recursive Feature Elimination, variable importance), cross-validation, and result visualization (confusion matrix, etc.).

The second day is fully dedicated to hands-on application with your own datasets, with personalized support. The course concludes with an introduction to neural networks and image recognition (classification of biological images).

Target: biologists with basic knowledge of R, who wish to analyze complex datasets using robust and interpretable machine learning methods.