CM / TD / TP. 36 hours (46.8 HTD), given in french.

Based on the same course given the previous year by Florian Yger

- The Elements of Statistical Learning,
*Trevor Hastie, Robert Tibshirani, Jerome Friedman*, Second edition. - Introduction au Machine Learning,
*ChloĆ©-Agathe Azencott*, Online version.

Available slides are in french.

Introduction. Notions of learning (supervised, unsupervised). Examples: Least squares and nearest neighbors.

Introduction to classification models. Nearest neighbors and Naive Bayes.

Linear methods for regression.

Linear methods for classification. Linear Discriminant Analysis and Logistic regression.

Evaluating a classifier, comparing classifiers. Metrics and Cross-validation.

Clustering. K-Means and Gaussian mixture model.

Hierarchical clustering