PSL week Spring Course 2024

C2MINES-07 Large-Scale Machine Learning

March 4-8, 2024

Mines Paris, 60 boulevard Saint-Michel, 75006 Paris Room L.109

This course is co-organized by Chloé-Agathe Azencott (MINES ParisTech & Institut Curie) and Fabien Moutarde (MINES ParisTech).

outline | schedule | grading | textbook | practical sessions


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 advanced 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.


  • Numerical Python (ie familiarity with programming in Python and the numpy, scipy, matplotlib librairies).
  • Basics of machine learning (such as the content of the Apprentissage Artificiel course for Mines Paris – PSL students).


Monday, March 4, 2024

  • 09:00 ­– 12:15 Lecture: Introduction to large-scale ML & optimization (K. Antonenko, CBIO Mines Paris – PSL) [slides (pdf)].
  • 13:45 – 17:00 Practical session: ML on large data with scikit-learn; this session will also contain an introduction to scikit-learn for those who have not used the library before.

Tuesday, March 5, 2024

  • 09:00 ­– 12:15 Lecture: Deep unsupervised learning and generative models (B. Sauvalle, CAOR Mines Paris – PSL) [slides (pdf)].
  • 13:45 – 17:00 Practical session: Deep learning, autoencoders and GANs with Python.

Wednesday, March 6, 2024

  • 09:00 ­– 12:15 Lecture: Natural Language Processing (NLP) with Recurrent Neural Networks and Transformers (A. Recanati, Sancare) [slides (pdf)].
  • 13:45 – 17:00 Practical session: NLP: word embeddings and RNNs.

Thursday, March 7, 2024

  • 09:00 ­– 12:15 Practical session: Stochastic Gradient Descent.
  • 13:45 – 17:00 Lecture: Systems for large-scale ML: focus on MapReduce (C.-A. Azencott, CBIO Mines Paris – PSL) [slides (pdf)].

Friday, March 8, 2024

  • 09:00 ­– 12:15 Lecture: Deep reinforcement learning (F. Moutarde, CAOR Mines Paris – PSL).
  • 13:45 – 17:00 Practical session: Deep reinforcement learning with Python.

All course materials will be in English but some lectures will be given in French.


If you are taking this class for credit (PASS/FAIL), you will be ask to turn in the notebooks of all your practical sessions.
Total credits: 2 ECTS.

Practical sessions

Practical sessions will take the form of Jupyter notebooks on the course github repo.

Please follow the instructions there to install Python3 and all the relevant packages. An alternative (sometimes preferable for deep learning notebooks) is to use Google Colab, for which you will need a Google account.

TAs: Alice Blondel (CBIO Mines Paris – PSL), Amandine Brunetto (CAOR Mines Paris – PSL), Simon De Moreau (CAOR Mines Paris – PSL), Waël Doulzami (CAOR Mines Paris – PSL), Gwenn Guichaoua (CBIO Mines Paris – PSL).


There is no single textbook for this course, but the following resources are relevant:

This course is not an introductory course to machine learning! If you want to learn the basics, or need a refresher, we recommend: