M1 Mathématiques et interactions - Site Evry

Apply for the degree
  • Places available
    25
  • Language(s) of instruction
    French
Présentation
Objectives

Développer des compétences en
- probabilités, processus stochastiques et mathématiques financières
- en programmation (python, R, C++,...)
- en statistiques et machine learning
- en analyse et modélisation

Le programme est axé sur l'idée de 3 parcours types débouchant sur une poursuite d'études dans les master 2 d'Evry : Data Sciences: santé, assurance, finance et Finance quantitative (ancien M2 IF), dans le M2 AMS de Paris Saclay, mais aussi, de façon plus marginale dans les M2 de Paris Saclay (MVA, statistique et machine learning, etc...)

Location
EVRY
Course Prerequisites

3 ème année de licence de Mathématiques ou écoles d'ingénieurs

Skills

  • 1- maitriser et mettre en oeuvre des outils et méthodes mathématiques de haut niveau.

  • 2- concevoir et rédiger une preuve mathématique rigoureuse.

  • 3- comprendre et modéliser mathématiquement un problème afin de le résoudre.

  • 4- analyser un document de recherche en vue de sa synthèse et de son exploitation.

  • 5- maitriser des outils numériques et langages de programmation de référence.

  • 6- expliquer clairement une théorie et des résultats mathématiques
    7- analyser des données et mettre en oeuvre des simulations numériques.

Post-graduate profile

3 ème année de licence de mathématiques appliquées

Career prospects

- M2 Statistiques
- M2 Data Sciences
- M2 machine learning
- M2 Finance Quantitative
- M2 Analyse, Modélisation, Simulation,
- M2 Algèbre Appliquée,
- M2 Mathématiques Vision, Apprentissage

Collaboration(s)
Laboratories

Laboratoire de Mathématiques et Modélisation d'Evry.

Programme
Subjects ECTS Lecture directed study practical class Lecture/directed study Lecture/practical class directed study/practical class distance-learning course Project Supervised studies
Anglais 2 18
Subjects ECTS Lecture directed study practical class Lecture/directed study Lecture/practical class directed study/practical class distance-learning course Project Supervised studies
Analyse et optimisation 7 27 30
Subjects ECTS Lecture directed study practical class Lecture/directed study Lecture/practical class directed study/practical class distance-learning course Project Supervised studies
Probabilités et Modèle linéaire 8 31.5 46.5
Subjects ECTS Lecture directed study practical class Lecture/directed study Lecture/practical class directed study/practical class distance-learning course Project Supervised studies
Introduction à l'apprentissage statistique et Méthodes numériques 5.5 21 21
Subjects ECTS Lecture directed study practical class Lecture/directed study Lecture/practical class directed study/practical class distance-learning course Project Supervised studies
Marchés et instruments financiers 2.5 24 0
Subjects ECTS Lecture directed study practical class Lecture/directed study Lecture/practical class directed study/practical class distance-learning course Project Supervised studies
Introduction à C++, Python et R 3 45
Subjects ECTS Lecture directed study practical class Lecture/directed study Lecture/practical class directed study/practical class distance-learning course Project Supervised studies
Anglais 2 18
Subjects ECTS Lecture directed study practical class Lecture/directed study Lecture/practical class directed study/practical class distance-learning course Project Supervised studies
Stage 12 12 36
Subjects ECTS Lecture directed study practical class Lecture/directed study Lecture/practical class directed study/practical class distance-learning course Project Supervised studies
Processus Stochastiques et Séries Temporelles 6 33 33
Subjects ECTS Lecture directed study practical class Lecture/directed study Lecture/practical class directed study/practical class distance-learning course Project Supervised studies
EDP, méthodes hilbertiennes et analyse numérique des EDP 5 24 24
Subjects ECTS Lecture directed study practical class Lecture/directed study Lecture/practical class directed study/practical class distance-learning course Project Supervised studies
Apprentissage Statistique avancé 2.5 12 12
Subjects ECTS Lecture directed study practical class Lecture/directed study Lecture/practical class directed study/practical class distance-learning course Project Supervised studies
Mathématiques Financières 2.5 12 12
Subjects ECTS Lecture directed study practical class Lecture/directed study Lecture/practical class directed study/practical class distance-learning course Project Supervised studies
Programmation avancée en C++ et VBA 2 24
Modalités de candidatures
Application period
From 15/01/2024 to 28/02/2024
From 10/04/2024 to 31/05/2024
From 02/06/2024 to 10/07/2024

Les dates indiquées ci-dessus sont uniquement valables pour la plateforme Inception. Les candidats qui dépendent de la plateforme MonMaster ne sont pas concernés.
Pour connaître la plateforme sur laquelle vous devez candidater, vous trouverez plus de renseignements sur la page Candidater à nos masters.

Compulsory supporting documents

  • Motivation letter.

  • All transcripts of the years / semesters validated since the high school diploma at the date of application.

  • Curriculum Vitae.

  • Detailed description and hourly volume of courses taken since the beginning of the university program.

Additional supporting documents

  • Certificate of French (compulsory for non-French speakers).

  • VAP file (obligatory for all persons requesting a valuation of the assets to enter the diploma).

  • Supporting documents :
    - Residence permit stating the country of residence of the first country
    - Or receipt of request stating the country of first asylum
    - Or document from the UNHCR granting refugee status
    - Or receipt of refugee status request delivered in France
    - Or residence permit stating the refugee status delivered in France
    - Or document stating subsidiary protection in France or abroad
    - Or document stating temporary protection in France or abroad.

Contact(s)
Course manager(s)
Arnaud GLOTER - arnaud.gloter@univ-evry.fr