List of courses by tracks

1st year

• APM_1S003_EP (MAA103) : Discrete Mathematics, Guillaume Dujardin, Houssna Zidani
• APM_1S006_EP (MAA106) : Introduction to Numerical Analysis, Maxime Breden
• APM_1S007_EP (MAA107) : Mathematical Modeling, Fabrice Djete

2nd year

• APM_2F005_EP (MAA205) : Algorithms for Discrete Mathematics, Lucas Gerin
• APM_2F010_EP (MAA210) :  Probability and Statistics, JM Bardet, Yoan Tardy
• APM_2S051_EP (MAA251) : Num Anal. 2: Linear Algebra and Optimization, Beniamin Bogosel, Teddy Pichard

3rd year

• APM_3F004_EP (MAA304) : Asymptotic Statistics, Eric Moulines
• APM_3F004_EP (MAA305) : Probability : stochastic processes, Quentin Cormier
• APM_3F004_EP (MAA307) : Convex optimization and Optimal control, Anne Auger
• APM_3F004_EP (MAA308) : Image analysis : Registration, Jean-Michel Roquejoffre
• APM_3S012_EP (MAA312) : Numerical Methods for ODEs, Michaël Goldman
• FMA_3F013_EP (MAA313) : Seminar : Mathematical Models, Maxime Breden

Bachelor Thesis, Mazyar Mirrahimi

For further information on Bachelor Program

1st year

• APM_3X061_EP (MAP361) - Randomness, Emmanuel Gobet

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) 

• APM_41012_EP (MAP412) -  Introduction to Numerical Analysis: from mathematical foundations to experimentation with Jupyter, Marc Massot
• APM_42031_EP (MAP433) - Statistics, Eric Moulines
• APM_41M01_EP (MAP471A)  - Modal -  Problem solving in applied mathematics, Lucas Gerin, Teddy Pichard

Period 2 (december to february)

• APM_42031_EP (MAP431) - Variational analysis of partial differential equations, Philippe Moireau
• APM_42032_EP (MAP432) - Modelling of random phenomena, Cyril Marzouk
• APM_42M01_EP (MAP472A) - Modal - Mathematical modelling through the experimental approach,  Beniamin Bogosel

Periode 3 (march to may)

• APM_43035_EP (MAP435) - Optimization and control, Grégoire Allaire
• APM_43M01_EP (MAP473B) - Modal - Dynamic systems, applications and simulations, Eric Gourdin
• APM_43M02_EP (MP473D) - Modal - Random numerical simulation around rare events, Gersende Fort

3rd year

Period 1 (september to december)

• APM_51050_EP (MAP550) - Théorie des jeux,  Charles Bertucci
• APM_51051_EP (MAP551) - Dynamic systems for modelling and simulation of multi-scale reactive media,  Marc Massot
• APM_51052_EP (MAP552) - Stochastic Models in Finance, Huyên Pham
• APM_51053_EP (MAP553) - Foundation of Machine Learning, Erwan Le Pennec
• APM_51055_EP (MAP555) - Signal processing, Rémi Flamary
• APM_51056_EP (MAP556) - Monte Carlo methods, Alain Durmus
• APM_51057_EP (MAP557) - Operational research: mathematical aspects and applications, Stéphane Gaubert 
• APM_51175_EP (MAP575) - EA - Advanced probability topics, Igor Kortchemski
• APM_5117_EP (MAP576) - EA - Learning theory, Matthieu Lerasle, Laurent Massoulié
• APM_51178_EP (MAP578) - EA - Emerging Topics in Machine Learning P1, Aymeric Dieuleveut, El Mahdi El Mhamdi

Period 2 (january to march)

• APM_52062_EP (MAP562) - Optimal design of structures, Beniamin Bogosel
• APM_52062_EP (MAP563) - Random modelling in biology, ecology and evolution, Vincent Bansaye
• APM_52062_EP (MAP564) - Social and communication networks: probabilistic models and algorithms,  Laurent Massoulié
• APM_52062_EP (MAP565) - Random and statistical process modelling, Mathieu Rosenbaum
• APM_52062_EP (MAP566) - Statistics in action, Julien Chiquet
• MDC_52067_EP (MAP567/MAT567) - Transport et diffusion, Grégoire Allaire, François Golse
• APM_52062_EP (MAP568) - Uncertainty management and risk analysis, Josselin Garnier
• APM_52062_EP (MAP569) - Regression and classification, Karim Lounici
• APM_52183_EP (MAP583) - EA - Deep learning from theory to practice, Kevin Scaman
• APM_52009_EP - ML for scientific computing , Haasna Zidani, Guillaume Dujardin
• MAP583 - EA (APM_52183)_EP - A Apprentissage profond, Kevin Scaman
• MAP588 - EA (APM_52188)_EP - Emerging Topics in Machine Learning P2, Rémi Flamary

Period 3 (april to august): research internship

• APM_52992_EP (MAP592) - Modelling and scientific computing, Ludovic Goudenège
• APM_52993_EP (MAP593) - Automation and Operational Research, Stéphane Gaubert, Frédéric Meunier
• APM_52994_EP (MAP594) - Probabilistic and statistical modelling, Aymeric Dieuleveut, Lucas Gerin
• APM_52995_EP (MAP595) - Financial mathematics,  Eduardo Abi Jaber, Charles-Albert Lehalle et Huyên Pham

• Data Science for Business - X and HEC, Erwan Le Pennec
• Data Science for Finance - X and HEC, Stefano de Marco
• Artificial Intelligence & Advanced Visual Computing, Simsekli Umut

 Some courses have benefited from a government grant managed by the ANR under France 2030 with the reference "ANR-22-CMAS-0002"

Applied Mathematics and Statistics

• M1 Applied Mathematics and statistics, Clément Rey
• M2 Data Science, Rémy Flamary, El Mhamdi El Mahdi
• M2 Mathematical modelling, Grégoire Allaire
• M2 Probability and Finance, Emmanuel Gobet

 Some courses have benefited from a government grant managed by the ANR under France 2030 with the reference "ANR-22-CMAS-0002"

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