CM / TD / TP. 36 hours (46.8 HTD), given in french.
Based on the same course given the previous year by Florian Yger
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