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1st year

 

2nd year

 

3rd year

 

Bachelor Thesis, Teddy Pichard

For further information on Bachelor Program

1st year

  • APM_3X061_EP (MAP361) - Randomness, Eva Loecherbach, 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) - Foundations of Machine Learning
  • APM_51056_EP 5MAP556) - Probability theory for ML: applications to Monte Carlo methods and generative models
  • APM_51059_EP ( MAP569) - Statistical Learning theory
  • CSC_51053_EP (INF553) - Database Management Systems
  • CSC_51054_EP (INF554) - Machine learning I
  • APM_51055_EP (MAP555) - Signal processing: from Fourier to Machine Learning
  • APM_51178_EP (MAP578) - Emerging Topics in Machine Learning - Collaborative Learning

Period 2 (january to march)

  • APM_52065_EP (MAP565) - Modélisation aléatoire et statistique des processus
  • APM_52066_EP ( MAP566) - Statistics in action
  • APM_52067_EP - Optimization for AI
  • APM_52068_EP ( MAP568) - Gestion des incertitudes et analyse du risque
  • APM_52070_EP - Mathematical Foundations of Decision Theory in AI
  • APM_52188_EP (MAP588) - Emerging Topics in Machine Learning -Optimal Transport and Interactions with Physical Sciences
  • CSC_52072_EP - Graph representaion learning
  • CSC_52081_EP (INF581) - Reinforcement Learning and Autonomous Agents
  • CSC_52083_EP (INF583) - Systems for Big Data
  • CSC_53432_EP - Natural Language Processing and Large Language Models
  • CSC_52087_EP (INF581A) - Advanced Deep Learning
  • APM_52183_EP (MAP583) - Apprentissage profond: de la théorie à la pratique
Period 3 (april to august): research internship
  • APM_52996_EP - Applied Mathematics for AI

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