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.

Evaluation

Non renseignéRendu des travaux pratiques + examen sur table

Programme

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 http://cazencott.info/index.php/pages/LSML-19-Large-Scale-Machine-Learning

Pré-requis

Numerical programming in Python + basics of Machine-Learning (roughly equivalent to content of the "Apprentissage artificiel" course for MINES ParisTech students). Machine learning basics; numerical python.

Références :
•     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.

Equipe enseignante

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