Mathematics, Machine Learning, Sciences, and Humanities: Master's Year 2

Program Objectives

A comprehensive training program in data science. The goal of this track 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.

Program 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.
Types of education
Initial training
French and English
ECTS Credits
60 credits
16 weeks
Type of Diploma
National diploma conferring the Master's degree
Academic Year

Educational team

  • Christian ROBERT

    University Professor

    Course supervisor

  • Robin RYDER

    Associate Professor

  • Nathalie HURTELOUP

    Training assistant

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