Large­Scale Machine Learning & Data Mining

Nombre de places: 100 - Langue: Français et anglais


Machine learning is a fast­growing field at the interface of mathematics, computer science and engineering, which provides computers with the ability to learn without being explicitly programmed, in order to make predictions or take rational actions. From cancer research to finance, natural language processing, marketing or self­driving cars, many fields are nowadays impacted by recent progress in machine learning algorithms that benefit from the ability to collect huge amounts of data and “learn” from them. The goal of this intensive 5­day course is to present the theoretical foundations and practical algorithms to implement and solve large­scale machine learning and data mining problems, and to expose the students to current applications and challenges of “big data” in science and industry.


The week is organized around two types of activities:
- Lectures (morning)
- Practical sessions (afternoon)

For more information, you can check the detailed program of the March 2019 edition of the course at


Students are expected to have working knowledge of basic linear algebra, probability, optimization and programming in Python. Since this is an ADVANCED course focusing on LARGE-SCALE issues in Machine-Learning, students should also ideally be already familiar with the basics of machine learning Therefore, this course is normally intended for students who have already attended the introductory course “Apprentissage artificiel” at MINES ParisTech, or any equivalent machine-learning basic course or MOOC.

This course is open in priority to 3rd ­year students at MINES ParisTech, and to all students and researchers at PSL pending on space availability.

Equipe enseignante

Chloé-Agathe AZENCOTT, MINES ParisTech (Centre de Bio-Informatique)
Fabien MOUTARDE, MINES ParisTech (Centre de Robotique)

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