Machine Learning is related to the design and study of systems that can learn by automatically extracting information from data. An example of application is classification, which consists of assigning an input data to a class. This course offers a broad introduction to Machine Learning and its applications to real problems. A subset of the following is covered: principal component analysis, linear discriminat analysis, bayesian decision theory, density estimation, ..., support vector machines, deep neural networks.
 Introduction [pdf]
 Rappels généraux et vocabulaire [pdf] [exercices]
 Eléments de théorie de l'apprentissage [pdf]
 Analyse en composantes principales [pdf] [exercices] [data.mat]
 Analyse factorielle discriminante [pdf] [exercices]
 Régression linéaire. Extensions à la régression logistique [pdf] [exercices]
 Méthodes décisionnelles bayésiennes
 Estimation de densité
 Support Vector Machines [pdf]
 Deep neural networks
