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List of courses by tracks
BACHELOR
1st year
- MAA103 : Discrete Mathematics, François Alouges
- MAA106 : Introduction to Numerical Analysis, Maxime Breden
- MAA107 : Mathematical Modeling, Vincent Bansaye
2nd year
- MAA203 : Introduction to Probability, Mathieu Rosenbaum
- MAA204 : Introduction to Statistics, David Métivier
- MAA205 : Algorithms for Discrete Mathematics, Lucas Gerin
- MAA208 : Numerical linear algebra, Teddy Pichard
- MAA209 : A first step in Numerical Optimization, Beniamin Bogosel
3rd year
- MAA304 : Asymptotic Statistics, Eric Moulines
- MAA305 : Probability : stochastic processes, Giovanni Conforti
- MAA307 : Convex optimization and Optimal control, Samuel Amstutz
- MAA308 : Image analysis : Registration, Stéphanie Allassonnière
- MAA309 : Image analysis : Segmentation, Stéphanie Allassonnière
- MAA312 : Numerical Methods for ODEs, Grégoire Allaire
- MAA313 : Seminar : Mathematical Models, Maxime Breden
Bachelor Thesis, Mazyar Mirrahimi
CYCLE INGENIEUR PROGRAM
1st year
- MAP361 - Randomness, Josselin Garnier, Nizar Touzi
This course introduces the basic notions of probability theory, that is the mathematical analysis of phenomena in which chance occurs. The teachers will insist in particular on the two major notions which are the foundations of this theory : conditioning and the law of large numbers.
The teaching aims at the acquisition of probabilistic reasoning and the learning of probabilistic modeling and simulation, as it is fundamental in many applications. The course is illustrated by examples and numerical experiments. It also introduces some notions of measure theory and it offers an opening towards statistics. During this teaching, the students will carry out a simulation project in pairs.
2nd year
Period 1 (september to november)
- MAP412 - Introduction to Numerical Analysis: from mathematical foundations to experimentation with Jupyter, Marc Massot
- MAP433 - Statistics, Eric Moulines
- MAP471A - Modal - Problem solving in applied mathematics, Lucas Gerin, Teddy Pichard
Period 2 (december to february)
- MAP431 - Variational analysis of partial differential equations, François Alouges
- MAP432 - Modelling of random phenomena, Thierry Bodineau
- MAP472A - Modal - Mathematical modelling through the experimental approach, Beniamin Bogosel
Periode 3 (march to may)
- MAP435 - Optimization and control, Grégoire Allaire
- MAP473B - Modal - Dynamic systems, applications and simulations, Ugo Boscain, Mazyar Mirrahimi
- MP473D - Modal - Random numerical simulation around rare events, Gersende Fort
3rd year
Period 1 (september to december)
- MAP551 - Dynamic systems for modelling and simulation of multi-scale reactive media, Marc Massot
- MAP552 - Stochastic Models in Finance, Nizar Touzi
- MAP553 - Foundation of Machine Learning, Erwan Le Pennec
- MAP555 - Signal processing, Rémi Flamary
- MAP556 - Monte Carlo methods, Emmanuel Gobet
- MAP557 - Operational research: mathematical aspects and applications, Stéphane Gaubert
- MAP558 - Automation with applications in robotics and quantum engineering, Ugo Boscain, Mazyar Mirrahimi
- MAP575 - EA - Advanced probability topics, Igor Kortchemski
- MAP576 - EA - Learning theory, Erwan Scornet, Matthieu Lerasle
- MAP578 - EA - Emerging Topics in Machine Learning P1, Aymeric Dieuleveut, El Mahdi El Mhamdi
Period 2 (january to march)
- MAP560 - Variational methods for computational fluid dynamics, Alexandre Ern
- MAP562 - Optimal design of structures, Samuel Amtutz
- MAP563 - Random modelling in biology, ecology and evolution, Vincent Bansaye
- MAP564 - Social and communication networks: probabilistic models and algorithms, Laurent Massoulié
- MAP565 - Random and statistical process modelling, Mathieu Rosenbaum
- MAP566 - Statistics in action, Julien Chiquet
- MAP567/MAT567 - Transport et diffusion, Grégoire Allaire
- MAP568 - Uncertainty management and risk analysis, Josselin Garnier
- MAP569 - Regression and classification, Karim Lounici
- MAP583 - EA - Deep learning from theory to practice, Marc Lelarge
- MAP586 - EA - Algorithms and software design principles for MAP in modern C++, Loïc Gouarin
- MAP588 - EA - Emerging Topics in Machine Learning P2, Rémi Flamary
Period 3 (april to august): research internship
- MAP591 - Signal and Image, François Alouges
- MAP592 - Modelling and scientific computing, Samuel Amstutz, François Alouges
- MAP593 - Automation and Operational Research, Stéphane Gaubert, Xavier Allamigeon
- MAP594 - Probabilistic and statistical modelling, Aymeric Dieuleveut, Vincent Bansaye, Randal Douc
- MAP595 - Financial mathematics, Nizar Touzi, Stéfano de Marco
GRADUATE DEGREES (MASTER OF SCIENCE AND TECHNOLOGY)
- Data Science for Business - X and HEC , Erwan Le Pennec
- Artificial Intelligence & Advanced Visual Computing, Erwan Scornet
MASTERS IP Paris
Applied Mathematics and Statistics
- M1 Applied Mathematics and statistics, Karim Lounici
- M2 Data Science, Rémy Flamary, Eric Moulines
- M2 Mathematical modelling, Grégoire Allaire
- M2 Probability and Finance, Emmanuel Gobet, Mathieu Rosenbaum
Mathematics and Applications
- M2 Mathematics of Randomness, Matthieu Lerasle
- M2 Mathematics for Life Sciences, Sylvie Méléard
- M2 Mathematics, Vision, Learning, Josselin Garnier
- M2 Optimization, Stéphane Gaubert