Statistical learning: from parametric to nonparametric models - WMM9AM78

Objectif(s)

This course is related to mathematical and statistical methods which are very used in supervised learning.

It contains two parts.

In the first part, we will focus on parametric modeling. Starting with the classical linear regression, we will describe several families of estimators that work when considering high-dimensional data, where the classical least square estimator does not work. Model selection and model assessment will particularly be described.

In the second part, we shall focus on nonparametric methods. We will present several tools and ingredients to predict the future value of a variable. We shall focus on methods for non parametric regression from independent to correlated training dataset. We shall also study some methods to avoid the overfitting in supervised learning.

This course will be followed by practical sessions with the R software.

Responsable(s)

Anatoli IOUDITSKI, Emilie DEVIJVER

Contenu(s)

Introduction.
Penalized linear methods for regression and classification.
Non linear methods for regression.
Cross Validation.

basic probability statistical inference, linear model.

Contrôle des connaissances

Evaluation : 50% de Projet (évaluation en continu et sur le rendu) et 50% de Examen écrit (2h00)

Rattrapage : Examen écrit (2h00)

CC = Ecrit / projet
CT = Ecrit (2h)
Rattrapage = Ecrit session 2 (2h)

L'examen existe uniquement en anglais

Calendrier

Le cours est programmé dans ces filières :

• Parcours de master - Master Informatique - Semestre 9 (ce cours est donné uniquement en anglais)
• Parcours de master - Master Math. et Applications - Semestre 9 (ce cours est donné uniquement en anglais)

Informations complémentaires

Code de l'enseignement : WMM9AM78
Langue(s) d'enseignement :

Vous pouvez retrouver ce cours dans la liste de tous les cours.