Machine Learning I

Ce cours présente une introduction générale à l'apprentissage automatique/statistique. Voici une liste (non-exhaustive) des sujets abordés : learning framework (training and test errors, model assessment and selection, bias/variance tradeoff, resampling methods, regularization), supervised learning (linear models, tree-based models, neural networks, parametric/non-parametric models), and unsupervised learning (dimensionality reduction).