Search by profile
Student Login
MENU

List of courses by tracks

1st year

 

2nd year

 

3rd year

 

Bachelor Thesis, Teddy Pichard

For further information on Bachelor Program

1st year

  • APM_3X061_EP (MAP361) - Randomness, Eva Locherbach, Julien Reygner

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.

  • APM_3X062_EP (MAP361P) - Python, Arvind Singh

 

2nd year

Period 1 (september to november) 

Period 2 (november to february)

Periode 3 (march to may)

 

3rd year PA MAP

Period 1 (september to december)

Period 2 (january to march)

Period 3 (april to august): research internship

3rd year PA IA (Apprentissage et IA)

Period 1 (september to december)

  • MDC_51006_EP (MAP553) - Foundation of Machine Learning, Erwan Le Pennec
  • APM_51056_EP (MAP556) - Probability theory for ML: applications to Monte Carlo methods and generative models - Alain Durmus
  • APM_51059_EP ( MAP569) - Statistical Learning theory - Karim Lounici
  • CSC_51053_EP (INF553) - Database Management Systems - Ioana Manolescu
  • CSC_51054_EP (INF554) - Machine learning I - Davide Buscali
  • APM_51055_EP (MAP555) - Signal processing: from Fourier to Machine Learning - Rémi Flamary
  • APM_51178_EP (MAP578) - Emerging Topics in Machine Learning - Collaborative Learning - Aymeric Dieuleveut, El Mhadi El Mahmdi

Period 2 (january to march)

  • APM_52065_EP (MAP565) - Modélisation aléatoire et statistique des processus - Mathieu Rosenbaum
  • APM_52066_EP ( MAP566) - Statistics in action - Zacharie Naulet
  • APM_52067_EP - Optimization for AI - Teddy Pichar, François Golse, Gaël Raoul
  • APM_52068_EP ( MAP568) - Gestion des incertitudes et analyse du risque - Josselin Garnier
  • APM_52070_EP - Mathematical Foundations of Decision Theory in AI - Luiz Chamon, Alain Durmus
  • APM_52188_EP (MAP588) - Machine learning with Optimal Transport and Algorithmic Fairness - Rémi Flamary
  • CSC_52072_EP - Graph representaion learning - Johannes Lutzeyer, Michalis Vazirgiannis
  • CSC_52081_EP (INF581) - Reinforcement Learning and Autonomous Agents - Patrick Loiseau, Jesse Read
  • CSC_52083_EP (INF583) - Systems for Big Data - Yanlei Diao
  • CSC_53432_EP - Natural Language Processing and Large Language Models - Guokan Shang
  • CSC_52087_EP (INF581A) - Advanced Deep Learning - Vicky Kalogeiton, Johannes Lutzeyer
  • APM_52183_EP (MAP583) - Apprentissage profond: de la théorie à la pratique - Bruno Galerne
Period 3 (april to august): research internship
  • APM_52996_EP - Applied Mathematics for AI - Aymeric Dieuleveut

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

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