MASTER IN MATHEMATICS AND DATA SCIENCE Mathematics, Vision, Learning (MVA)

Language(s) of instruction: French (60%), English (40%)

Length of study: 1 year, full time

Partner:  Université Paris-Saclay, ENS Paris-Saclay, Institut Polytechnique de Paris, Université Paris Cité

Course Location: ENS Paris-Saclay, Paris (Université Paris Cité, ENS Ulm), Gif-sur-Yvette, Palaiseau

Degrees awarded: Master 2 Mention Mathématiques et Applications, Parcours Mathématiques, Vision, Apprentissage

Entry Requirements: Admission is based on application review. Recruitment takes place at the M1 level or equivalent. We mainly admit:
– Students holding an M1 (Master’s Year 1) in mathematics (applied or pure), computer science, data science, artificial intelligence, etc.
– Students in third year of their engineering school

Language pre-requisites: Proof of English proficiency is required: TOEFL, TOEIC, or a personal statement, which may be supported by letters of recommendation and any relevant documents considered useful.

Course overview

The Master 2 MVA (Mathematics, Vision, Learning), created by the mathematics department of the ENS Paris-Saclay, is a unique master in France since its creation in 1996. In cooperation with several academic partners, it trains a large number of university and « grandes ecoles » students each year in Research, Development and Innovation for public and private organizations and companies in the field of mathematics applied to data, image and signal processing.
The huge growth in the use of digital data in all fields of science, technology and society requires the training of high-level mathematical researchers mastering the acquisition and processing of digital data on the one hand, and their automatic interpretation on the other. These two aspects are strictly complementary and are reflected in the three terms characterizing the MVA program.

Skills and competencies developed
  • Master and apply advanced mathematical tools and methods.
  • Understand and mathematically model a problem in order to solve it.
  • Analyze data and conduct numerical experiments.
  • Analyze a research paper for synthesis and practical application.
  • Master standard digital tools and programming languages.
  • Clearly and rigorously explain and write about mathematical theories and results.

Contact

Pedagogical contacts
Ms. Anne SABOURIN
anne.sabourin@u-paris.fr

Ms. Stéphanie ALLASSONNIERE
stephanie.allassonniere@parisdescartes.fr

Administrative coordinator
masters@mi.parisdescartes.fr

Website