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, March 26th:
**Introduction to large-scale ML**by Jean-Philippe Vert (Mines/Institut Curie/ENS) - Tuesday, March 27th:
**Systems for large-scale ML**by Chloé Azencott (Mines/Institut Curie) - Wednesday, March 28th:
**Deep learning 1, deep learning 2**by Fabien Moutarde (Mines) - Thursday, March 29th:
**Statistical machine learning and convex optimization**by Francis Bach (INRIA/ENS) - Friday, March 30th:
**Randomized techniques for large-scale ML**by Jean-Philippe Vert (Mines/Institut Curie/ENS)

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:

- Mining of massive datasets by Leskovec, Rajaraman and Ullman
- Deep learning by Goodfellow, Bengio and Courville
- Large-Scale Optimization: Beyond Stochastic Gradient Descent and Convexity by Sra and Bach

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 (Mines)
- Akin Kazakci (Mines)
- Fabien Moutarde (Mines)
- Jean-Philippe Vert (Mines/ENS)