Large ­Scale Machine Learning & Data Mining

Objectifs

The goal of this week 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.

Evaluation

The course is graded on a combination of practical work (notebooks from the afternoon sessions) and a final exam on the last afternoon of the course.

Programme

The course is organized as a combination of morning lectures (9:00-12:15) and afternoon practicals (13:45-17:00) in Python. The last afternoon is devoted to a final exam.
The lectures are taught by Chloé-Agathe Azencott (CBIO Mines Paris) and Fabien Moutarde (CAOR Mines Paris) as well as one or two guest lecturers. The practicals are taught by PhD students and postdocs from Mines Paris.

Pré-requis

This week is meant for students who are *already familiar* with machine learning. More specifically, students are expected to be comfortable with:
- Numerical Python (ie familiarity with programming in Python and the numpy, scipy, matplotlib librairies).
- Basics of machine learning (such as the content of the S1333-5 Apprentissage Artificiel course for MINES ParisTech students).

Equipe enseignante

MOUTARDE Fabien, AZENCOTT Chloé-Agathe