Goals: The students will be introduced to the basic statistical and algorithmic concepts in the field of Machine Learning, especially in the context of current research in bioinformatics, biology and biotechnology. They will work on several practical problems and implement / use the methods learned during the lectures to extract information from biological datasets in R. In particular, they will learn how to process data, choose the appropriate model to answer specific questions, evaluate and communicate the results. The students will be assigned weekly exercises which they have to complete. Presenting in turns the results from the exercises, in addition to a final oral exam, are prerequisites to pass the course. Content: - Pre-processing of biological data and model implementation with R - Classification metrics and permutation approaches - Linear Models for Regression and Classification - Kernel Methods for Regression and Classification - Feature Selection - Semi-supervised learning / active learning - Classification trees and Random Forests - Graphical models