M2 Mathematics, Vision, Learning

Master's degree

Specialisation Mathematics and applications

Formation initiale

English

French

The MVA is a 2nd year Master's Research programme which started in 1996, run by the mathematics department of ENS Paris-Saclay, co-accredited with the Institut Polytechnique de Paris and shared with Université Paris Cité. The Master's in MVA is unique in France for its scientific focus on mathematical interfaces and its openness to an innovation ecosystem that brings together academic laboratories, research organisations and companies in France and abroad.

Courses from mid-September to mid-April Internship from mid-April for 4 to 6 months

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Information

Présentation

Skills

Master and apply cutting edge mathematical tools and methods.

Objectives

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.

Calendar

Start of programme (indicative date)

21/09/2026

Admission

Capacity

Available Places

180

Target Audience and Entry Requirements

Admission: On a case-by-case basis, for holders of an M1 in mathematics, informatics or physics, or for 3rd-year engineering school students (potentially admitted for dual programmes, according to agreements).

Application Period(s)

Inception Platform

From 01/05/2026 to 30/06/2026

Supporting documents

Compulsory supporting documents

Copy of the last diploma.

Copy of identity document.

Motivation letter.

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

Curriculum Vitae.

Certificate of English level (compulsory for non-English speakers) or GMAT / GRE test results.

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

Additional supporting documents

Letter of recommendation or internship evaluation.

Certificate of English level (compulsory for non-English speakers).

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

Document indicating the list of local M2 choices available here : https://urlz.fr/i3Lo.

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.

Programme

Subjects	ECTS	Semester	Lecture	directed study	practical class	Project
Deep Learning	5	Semester 1	24

Fondements Théoriques du deep learning	5	Semestre 1	25		5

Audio Signal Processing - Time-Frequency Analysis	5	Semester 2	21

Graphical models : Discrete inference and learning	5	Semestre 2	24

Deformable Models and Geodesic Methods for Image Analysis	5	Semester 2	21		9

Deep Learning in Practice	5	Semester 2	12	12

Point clouds and 3D modelling	5	Semestre 2	16		16

Optimal transport for machine learning	5	Semester 1	18

Algorithms and learning for protein science	5	Semester 2	18		3

Algorithms for speech and natural language processing	5	Semestre 2	21

Machine learning for time series	5	Semester 1	27	0	9

Séminaire Turing - Sûreté et interprétabilité des IA à usage général	2.5	Semestre 1	12

Bayesian machine learning	5	Semester 2	16		12

Geometry processing and geometric deep learning	5	Semester 1	12		14

Audio signal Analysis, Indexing and Transformations	5	Semester 2	14		10

Deep Learning for Image Restoration and Synthesis	5	Semester 2	24		12

3D Computer Vision	5	Semester 1	18	3

Interactions (Complexity, Stochasticity, Extrema & Rare events)	5	Semester 1	24

Biostatistics	5	Semester 2	24			3

Inverse problems and imaging: statistical and stochastic approaches	5	Semester 2	27

Responsible machine learning	2.5	Semestre 1	10

Geometric Data analysis	5	Semester 1	14		7

Fundamentals of reproducible research and free software	5	Semester 1	12	3	9

Statistical learning with extreme values	5	Semester 1	15	6	4

Apprentissage profond et traitement du signal, introduction et applications industrielles	5	Semestre 1	12		18

The machine intelligence of images	5	Semester 1	12		18

Introduction à l'apprentissage statistique pour les géosciences	5	Semestre 2	12		18

Algorithmes pour l'optimisation et la gestion des réseaux	5	Semestre 2	12		18

Stopping times and online algorithms	5	Semester 1	24

Immersion en hôpital - collaboration en binôme avec un médecin	5	Semestre 1

Robotics	5	Semester 1	19	9

Regulating AI	2.5	Semestre 2	12

Modèles génératifs pour l'image	5	Semestre 2	18	9

Intelligence artificielle et environnement	2.5	Semestre 2	9		9

Convex optimization and applications in machine learning	5	Semester 1	21

Functional Brain Imaging and Brain Computer Interfaces	5	Semester 2	24

Geometry and Shape Spaces	5	Semester 2	21		9

Reproducible research project	5	Semester 2		9	12

Deep learning for medical imaging	5	Semestre 2	27

Topological data analysis for imaging and machine learning	5	Semester 1	12		12

Sub-pixel Image Processing	5	Semester 1	20		16

Remote sensing data: from sensor to large-scale geospatial data exploitation	5	Semester 2	31.5

Introduction to Probabilistic graphical models and deep generative models	5	Semester 1	30

Sequential learning	5	Semester 2	18

Some Mathematical methods for neurosciences	5	Semester 1	24	16

Kernel Methods for machine learning	5	Semester 2	23

Introduction à l'imagerie numérique	5	Semestre 1	21		6

Théorie de la détection et ses applications industrielles	5	Semestre 2	24

Random matrix theory	5	Semester 2	24		6

Object Recognition and Computer Vision	5	Semester 1	30

Introduction to statistical learning	5	Semester 1	16	8

Medical Image Analysis based on generative, geometric and biophysical models	5	Semester 1	24

Computational statistics	5	Semester 1	20		20

Advanced learning for text and graph data ALTEGRAD	5	Semester 1	14		14

Deep reinforcement learning	5	Semester 1

Image denoising : the human machine competition	5	Semester 1	20	10

Graphs in Machine Learning	5	Semester 2	16	12

Foundations of distributed and large scale computing optimization	5	Semester 1	24

Large Language Models for code and proof	5	Semester 2	12	12

Génération de données en IA par transport et débruitage	5	Semestre 2	27

AI and Computer vision for cultural heritage	2.5	Semester 2	12

Representation learning for computer vision and medical imaging	5	Semester 2	12	12

Stochastic calculus in machine learning	5	Annualized	24

Multimodal Explainable AI (XAI)	5	Annualized	10	17		2

Training and Deploying Large-Scale models	5	Annualized	14	14		4

Subjects	ECTS	Semester	Lecture	directed study	practical class	Lecture/directed study	Lecture/practical class	directed study/practical class	distance-learning course	Project	Supervised studies
Internship	20	Semestre 2

Teaching Location(s)

GIF SUR YVETTE

PARIS 05

PARIS 06

PARIS 14

PALAISEAU

Contact

Laurent Oudre

Contact
Gwladys Stouvenel

Contact