PSL week Spring Course 2018

Large-Scale Machine Learning

March 26-30, 2018

Mines ParisTech, 60 boulevard Saint-Michel, 75006 Paris


Machine learning is a fast-growing 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 self-driving 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 5-day course is to present the theoretical foundations and practical algorithms to implement and solve large-scale machine learning and data mining problems, and to expose the students to current applications and challenges of “big data” in science and industry.


Morning lectures (room L118)

There will be a 3h lecture every morning, 9:30am-12:30pm. Lectures will be in French.

Afternoon practical sessions (rooms L117-L119-L120)

Each afternoon, 2pm-5:30pm, students will work on exercices and practical sessions.

Click on the link above for informations and material


Registration is closed

Participants are expected to have working knowledge of basic linear algebra, probability, optimization and programming in Python. Ideally, a prior exposure to a basic machine learning course is a plus, such as the ES2A “Apprentissage artificiel” for MINES ParisTech students.

Everyone is welcome to register, however we may have to limit and select participants pending on space availability. Priority will be given to students and researchers from PSL.


60% final written exam; 40% practical session

Total credits: 2 ECTS


There is no single textbook for this course, but the following ressources are relevant:

I want an introductory course on machine learning!

This course is not an introductory course on machine learning. If you want to learn the basics, we recommend for example: