X5I13719 - Statistical learning -

This series of lectures aims at describing the main problems data scientists and machine/statistical learners have to address (data visualization, dimension reduction, clustering, classification, prediction/regression tasks). For each of these tasks, we will cover several basic strategies that should serve as reference tools at the beginning of any analysis. 


The coming lectures are not "theoretical" ones since they do not contain systematic proofs of invovled (but still nice! ) theoretical results.

But their goal is nevertheless to provide guidelines (based on theoretical considerations) for a deeper understanding of the strategies that will be discussed.

For example, the best results are almost never achieved with the default choice of the parameters values. Tuning them carefully depending on the context is what makes the learning strategy work well.

Informations sur l'espace de cours

Nom Statistical learning - MMMEF
Nom abrégé UP1-C-ELP-X5I13719-01 - MMMEF
EnseignantsCelisse Alain
Groupes utilisateurs inscrits Consultation des ressources, participation aux activités :
  • [2021] MIX505 - Master 2 Indifférencié Modélisation et Méthodes Mathématiques en Economie et Finance (MMMEF) parcours ESCP Europe (diploma-MIX505-2021)
  • [2021] UFR 27 - Matière : Master 2 MMMEF (groups-mati27EM0315-2021)
Consultation des ressources uniquement : aucune cohorte inscrite.

Rattachements à l'offre de formation

Élément pédagogique UP1-C-ELP-X5I13719 - Statistical learning
Chemin complet > Année 2021-2022 > Paris 1 > Mathématiques et informatique > M2 Ind Modélisation et Méthodes Math. en Economie et Finance > Semestre 3 > UE1 Cours fondamentaux (prendre 20 crédits) > Choix 6 cours (2 de 5 ECTS et 4 de 2.5 ECTS) > Choix 4 cours de 2.5 ECTS > Statistical learning