Ects : 5

Enseignant responsable :

• YANN CHEVALEYRE

Volume horaire : 36

Description du contenu de l'enseignement :

Volume horaire : CM : 18h TD : 18h

1. Introduction
   1. What is Machine Learning
   2. A simple method: k-nearest neighbors
   3. Evaluation of classifiers
   4. Maximum Likelihood and Maximum A posteriori
2. Generative Learning
   1. Maximizing the Likelihood of the examples
   2. Linear Discriminant Analysis and Naive Bayes
3. Discriminative Learning
   1. Maximizing the likelihood and the a posteriori probability of labels
   2. Logistic Regression
   3. Stochastic gradient descent (SGD)
   4. SGD for generalized linear models
   5. Beyond linearity: kernelization of the SGD
4. Unsupervised Learning
   1. Learning latent models: the Expectation-Maximization Algorithm
   2. clustering: k-means, DBSCAN
   3. Learning probability density functions: mixtures of gaussians
5. Introduction to Bayesian Learning
   1. Bayesian Linear Regression
   2. Laplace method
6. Introduction to Neural Networks

Pré-requis recommandés :

- Connaissances de base en statistiques et algèbre linéaire

Compétence à acquérir :

Understand most useful machine learning algorithms

Mode de contrôle des connaissances :

CC+Examen

Bibliographie, lectures recommandées

- Friedman, Tibshirani, Hastie. The Elements of Statistical Learning - Chloé Azencott. Introduction au Machine Learning - Cornuéjols, Miclet. Apprentissage artificiel: Concepts et algorithmes