Cours de Licence 1 Mathématiques Informatique à Dauphine

Artificial Intelligence, Systems, Data - Mathematics Track - Master's Year 2

Program Objectives

A comprehensive training program in data science. The goal of the Master in Artificial Intelligence, Systems and Data (IASD) - Mathematics track (formerly MASH) is to offer students with an academic background in mathematics a solid education in statistics as they apply to the new economy, digital science, and the humanities writ large. It stems from the exponential increase in the amount of data generated across fields as varied as biology, medicine, e-commerce, imaging, video production, and language processing.

Training objectives :

  • Master the theoretical foundations of machine learning, including kernel methods, supervised and unsupervised learning, optimization, graphical models, etc.
  • Master foundational statistical methods such as simulation, estimation, detection, etc.
  • Understand the application of machine learning in marketing, health, journalism, public policy, etc.
  • Gain working knowledge of certain key programming languages: Python (particularly the scikit-learn library), Hadoop, R, MATLAB, Julia, etc.
  • Gain practical experience with manipulating data sets produced by applications and projects.


Courses are held at 16 bis rue de l'Estrapade, 75005 Paris.

By joining this Master’s program, you also become part of the Université PSL. Ranked among the top 50 universities worldwide (THE, QS), PSL offers graduate-level programs of excellence, at both the Master's and Doctoral levels, based on the scientific strengths of all its member institutions. The degree is prepared at Université Paris Dauphine-PSL and awarded by Université PSL.

State-controlled national master's degree
  • Types of education

    Initial training

  • ECTS Credits

    60 credits

  • Capacity

    35

  • Academic Year

    2026/2027

  • Language(s)

    French and English

  • Internship

    16 weeks

  • Type of Diploma

    National diploma

Educational team