Master 1

Applied Mathematics and Statistics


The first year proposes a wide offering of basic and more specialized courses in applied mathematics that will allow students to build a personalized curriculum adapted to their academic and professional projects in the following areas :
– Statistics, Finance and Actuarial science
– Modeling, Probability, Artificial Intelligence
– Optimization
– Signal, Computing, Machine Learning
– Numerical analysis and EDP
It also includes a PhD Track whose purpose is to guide the students towards the doctorate (after the end of the Master).

Language of instruction: French. Some courses can be given in English.
ECTS: 60
Oriented: Industry or research
Duration: 1 year
Courses Location: IP Paris

Educational objectives

The aim of the M1 Applied Mathematics is to equip students with a solid foundation in applied mathematics in order to pursue doctoral studies or directly apply to the many positions available to young mathematicians in academy, business or industry. The interaction with research is also present throughout the curriculum in several forms: seminars, mentoring projects, internships in research labs or companies to allow students to discover, from within, research activities in applied mathematics by addressing current, open problems

Program structure

Each course listed below corresponds to 5 ECTS. The evaluation is done either on the basis of an oral or written exam, either on the realization of a personal project.

Ecole polytechnique:
• MAP551 Systèmes dynamiques pour la modélisation et la simulation des « milieux réactifs » multi-échelles (M. Massot)
• MAP552 Modèles stochastiques en Finance (N. Touzi)
• MAP553 Regression (K. Lounici)
• MAP555 Signal Processing (O. Rioul)
• MAP556 Stochastic simulation and Monte-Carlo methods (E. Gobet)
• MAP557 Recherche opérationnelle : aspects mathématiques et applications (S. Gaubert)
• MAP572 Mise en oeuvre de méthodes numériques
• MAP573 R pour les statistiques
• MAP575 Fondements des probabilités et applications

ENSTA Paris:
• IN207 : Bases de données
• ANN201 : La méthode des élements finis
• OPT201 : Optimisation différentiable
• PRB201 : Chaînes de Markov
• STA201 : Modélisation statistique
• PRB202 : Martingales
• RO201 : Recherche opérationnelle
• ANA201 : Analyse fonctionnelle

ENSAE Paris:
• Théorie des probabilités
• Econométrie 1
• Statistique 1
• C++
• Python pour le Data scientist
• Introduction aux processus
• Instruments Financiers
• Microéconomie

Telecom Paris:
• Introduction to statistics
• Hilbert spaces, mathematical statistics and Probability
• Martingales and asymptotic statistics
• Introduction to time series
• Linear models in statistics
• Optimization for Machine learning

Télécom Sud Paris:
• Introduction to machine learning
• Bayesian filtering in hidden Markov models
• Calcul scientifique
• Statistique appliquée
• Compression, codage et modulation pour les systèmes de communications
• Traitement du signal avancé
• Introduction à la Bioinformatique

Ecole Polytechnique:
• MAP560 Variational Methods for Coputional Fluid Dynamics (F. Alouges)
• MAP561 Automatic Control with applications in Robotics and in Quantum Engineering (U. Boscain-M. Mirrahimi)
• MAP562 Optimal Design of Structures (G. Allaire)
• MAP563 Modèles aléatoires en écologie et évolution (S. Méléard)
• MAP564 Communication Networks and Social Networks : probabilistic models and algorithms / réseaux sociaux et de communication : modèles et algorithmes probabilistes (L.Massoulié)
• MAP565 Modélisation statistique (M. Rosenbaum)
• MAP566 Statistics in Action (M. Lavielle)
• MAP/MAT567 Transport et diffusion (G. Allaire & F. Golse)
• MAP568 – Gestion des incertitudes et analyse de risque (J. Garnier)
• MAP569 Machine Learning II (E. Le Pennec)MAP583 Apprentissage profond
• MAP584 Mise en oeuvre effective de la méthode des éléments finis
• MAP585 Théories de l’apprentissage
• MAT/MAP587 Transport et diffusion

ENSTA Paris:
• ANN202 : Approximation d’EDP par éléments finis
• STA202 : Séries chronologiques
• PRB203 : Calcul stochastique
• OPT202 : Optimisation différentiable 2
• SIM203 : Calcul scientifique à haute performance
• PRB210 : Modèles stochastiques pour la finance
• STA203 : Apprentissage statistique
• ANA202 : Théorie spectrale
• RO203 : Jeux, graphes et recherche opérationnelle
• PRB220 : Méthodes numériques probabilistes
• STA210 : Méthodes de Monte-Carlo et Méthodes de rééchantillonnage

ENSAE Paris:
• Econométrie 2
• Séries temporelles linéaires
• Introduction au machine learning
• Statistique 2
• Introduction à la finance mathématique
• Simulation et Monte Carlo
• Théorie du risque

Telecom Paris:
• Advanced Statistics
• Machine learling
• Machine Learning for Text Mining
• Numerical analysis
• Random processes in continuous time and stochastic calculus

Télécom Sud Paris:
• Apprentissage statistique
• Inférence bayésienne dans des modèles markoviens
• Processus stochastiques
• Statistiques paramétriques et valeurs extrêmes
• Vision par ordinateur et deep learning
• Pattern recognition and biometrics
• Methodes de Monte Carlo
• Introduction to hypothesis testing and sampling theory

Phd track in Data Science and Artificial Intelligence: Additional activities including research seminar, research mentoring by Professors of IPP, a summer school to prepare students to carry out cutting edge research in Data Science and Artificial.

Internship (April-July): 20 ECTS.

IP Paris labs involved

LTCI: Information Processing and Communications Laboratory (Télécom Paris),
SAMOVAR (Télécom SudParis),
CMAP: Applied Mathematics Center (Ecole Polytechnique),
CREST: Center for Research in Economics and Statistics (ENSAE),
UMA: Applied Mathematics UER (ENSTA)

Career prospects

It is highly recommended to follow a Master 2 in fundamental or applied mathematics.
The wide variety of courses, seminars, projects and internships proposed during the master offers the
possibility, for every student, to build his/her own curriculum in mathematical sciences.
After M2 graduation, the student can apply for PhD funding in top research labs or apply for jobs with solid scientific content.

Industrial partners

• Aérospatiale,
• Airbus,
• Air Liquide,
• CEA,
• Danone,
• Dassault,
• EDF,
• EuroDecision,
• GDF,
• General Electrics,
• Ingenico,
• Peugeot,
• Renault,
• RTE,
• Sanofi,
• Thalès,
• Véolia,
• Xerox


Application guidelines for a master’s program at IP Paris

Academic prerequisites:
Bachelor in mathematics, mathematical sciences or related field.

Language prerequisites: French or English (for some courses)

Application timeline
Deadlines for the Master application sessions are as follows:
– First session: February 28, 2020
– Second session: April 30, 2020
– Third Session (optional): June 30, 2020 (only if there are availabilities remaining after the 2 first sessions)
Applications not finalized for a session will automatically be carried over to the next session.

You shall receive an answer 2 months after the application deadline of the session.

Tuition fees

International Master: EU students : 4250 euros / Non-EU students: 6250 euros


Francois Alouges ; Frédéric Jean ; Pascal Bianchi ; Guillaume Lecue