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.

- Monday 20th:
**Introduction to large-scale ML**by Jean-Philippe Vert (Mines/Institut Curie/ENS) - Tuesday 21st:
**Statistical machine learning and convex optimization**by Francis Bach (INRIA/ENS) - Wednesday 22nd:
**Systems for large-scale ML**by Laurent Laudinet (Thales) - Thursday 23rd:
**Deep learning**by Fabien Moutarde (Mines) - Friday 24th:
**Large-scale optimization for ML**by Marco Cuturi (ENSAE)

Each afternoon, **2pm-5:30pm**, students will participate in a RAMP practical session to implement ML algorithms, solve a challenge... and get a grade.

**IMPORTANT: Click here for instructions about the practical sessions: please download and install the data and packages before the first practical sessions.**

**Registration is free but required. Deadline: March 13, 2017**.

~~MINES ParisTech students should register through the standard online systems~~~~Other participants should register here~~**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.

Credits: 2ECTS

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

- Video: Introduction to machine learning (in French) by Chloé Azencott
- Video: Machine learning (in English) by Andrew Ng
- Book: The elements of statistical learning by Hastie, Tibshirani and Friedman
- Book: Pattern recognition and machine learning by Bishop

- Chloé-Agathe Azencott
- Akin Kazakci
- Fabien Moutarde
- Philippe Mouttou
- Jean-Philippe Vert