Large­Scale Machine Learning & Data Mining

Objectifs

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.

Programme

The week is organized around three types of activities:
- Lectures (morning)
- Practical sessions (afternoon)
- Conference and round tables (evenings)

Pré-requis

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

Equipe enseignante

Fabien MOUTARDE, MINES ParisTech
Jean-Philippe VERT, MINES ParisTech