Cerfacs Enter the world of high performance ...

From 27 April 2020 to 30 April 2020

Machine learning for data science

nasri |  

Deadline for registration: 15 days before the starting date of each training
Duration : 4 days / (28 hours)



This training course enables the participants to reinforce their theoretical and practical knowledge in order to implement machine learning techniques for the automatic analysis of data. The main statistical methods for data analysis are presented, both for data exploration (non-supervised learning) and for prediction (supervised learning). Each method is first presented and commented on a theoretical level, and then illustrated on numerical experiments run with public datasets using R and/or python/scikit-learn software.

Objective of the training

To know the main algorithms of automatic data analysis, and to know how to use them with R and/or python/scikit-learn.

Learning outcomes

The participants should be able to :

  • recognize the type of problem that they are facing (supervised or non-supervised learning, sequential learning, reinforcement learning…);
  • choose the right algorithm to use;
  • use an R on python implementation of this algorithm.

Target participants

This training session is for students, engineers, and computer scientists who wish to reinforce or extend their theoretical background and practical knowledge on automatic data analysis by statistical learning algorithms.


Basic knowledge in statistics: elementary probability, statistical tests, Gaussian linear model.

Basic knowledge in algorithmic and programming.

Install Python 2.7 with Anaconda, R 3.4.2 and IRkernel. Internet access during the sessions in order to get possible updates or to load additional libraries.

In order to verify that the prerequisites are satisfied, the following questionnaire must be completed. You need to get at least 75% of correct answers in order to be authorized to follow this training session. If you don’t succeed it, your subscription will not be validated. You only have two chances to complete it.

Questionnaire 1 https://goo.gl/forms/xL86TzPDFOC5r7ln1

Scientific contacts: Philippe Besse, Sébastien Gerchinovitz, Béatrice Laurent-Bonneau


  • Traines/PhDs/PostDocs : 280 €
  • Cerfacs shareholders/CNRS/INRIA : 800 €
  • Public : 1600 €


Every day from 9h to 17h30.

Morning: lecture; afternoon: hands-on sessions.

Day 1

General presentation of statistical machine learning and its main approachs. Comparison with traditional statistics and machine learning.
Unsupervised learning:
– Principal component analysis
– Agglomerative Hierarchical Clustering
– k-means, k-medoids and variants
– overview of other methods : Affinity Propagation, dbscan, etc.
Day 2
Supervised learning 1 / 2 :
– k nearest neighbors
– Gaussian linear model, logistic regression, model selection
– LASSO et variants
– Support Vector Machines
Day 3
Supervised learning 2 / 2 :
– Decision Trees
– Bagging, Random Forests, Boosting
– Neural networks, deep learning
Day 4
Sequential learning, multi-armed bandit problems
Super-learning and expert aggregation
Reinforcement learning (introduction)

Final examination

A final exam will be conducted during the training.





THESIS PRIZE PAUL CASEAU, Thibaut LUNET, November 13, 2019

Brigitte Yzel |  14 November 2019

THESIS PRIZE PAUL CASEAU 2019 was awarded on November 13, 2019 by EDF and National Academy of Technologies of France to M. Thibaut LUNET doctoral student of the ALGO team, for his thesis defended on 9 January 2018 and entitled "Space-time parallel strategies for the numerical simulation of turbulent flows"  Read more

A PhD student awarded of the “Outstanding Student Poster” of EGU

superadmin |  6 November 2019

Rem-Sophia MOURADI, PhD student at CERFACS  and EDF R&D Hydraulic Department, has been awarded of the "Outstanding Student Poster", during the EGU General Assembly 2019 (European Geosciences Union). This international conference gathers each year about 16 000 scientists with 6 000 oral presentations and 10 000 posters. Among the 2 500 posters presented by PhD students, fifty only are awarded with this price, that to say 2%. A combined orthogonal decomposition and polynomial chaos methodology for data-based analysis and prediction of coastal dynamics (Mouradi, R.-S.; Goeury, C.; Thual, O.; Zaoui, F.; Tassi, P.)Read more