Large-Scale Machine-Learning

80

English

Mines Paris (?)

The goal of this week is to present the theoretical foundations and practical algorithms to implement and solve LARGE-SCALE machine learning problems, and to expose the students to current applications and challenges of "big data" in science and industry.

The course is organized as a combination of morning lectures (9:00-12:15) and afternoon practicals (13:45-17:00) in Python. ;
For more details, you can check the web page of the 2023 edition at https://people.minesparis.psl.eu/fabien.moutarde/ES_LSML/LSML-23_LargeScaleMac hineLearning.htm (even if the detailed program may change every year).
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.

The course is validated based on practical work (notebooks from the afternoon sessions).

This is an ADVANCED course in Machine-Learning;
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 "Apprentissage Artificiel" course of MINES Paris, https://moodle.psl.eu/enrol/index.php?id=17045).

MOUTARDE Fabien

AZENCOTT Chloé-Agathe;
MOUTARDE Fabien