Cerfacs Enter the world of high performance ...

From 10 October 2022 to 13 October 2022

Machine learning for data science

Jean-Christophe JOUHAUD |  

Training session common to CERFACS/INSA Toulouse

 

               

Cerfacs is Qualiopi certified for its training activities

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

Before signing up, you may wish to report us any particular constraints (schedules, health, unavailability…)

at the following e-mail address : training@cerfacs.fr

Satisfaction index

In October 2021, 90,9% of participants were satisfied or very satisfied

(results collected from 11 respondents out of 12 participants, a response rate of 91,6%)

Abstract

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.

Teaching methods

The training is an alternation of theoretical presentations and practical work. A multiple choice question allows the final evaluation. The training room is equipped with computers, the work can be done in sub-groups of two people.

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.

Prerequisites and registration

  • 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.
  • The training can take place in French or English depending on the audience, level B2 o f CEFR is required.

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

After completing the pre-requisite tests and obtaining at least 75% correct answers, you can register:

Pre-registration

Referent teacher: Jean-Christophe JOUHAUD

Fee

  • Traines/PhDs/PostDocs : 504 € excl. tax
  • Cerfacs shareholders/CNRS/INRIA/INSA Toulouse : 1260 € excl. tax
  • Public : 2520 € excl. tax

Program

Every day from 9h to 17h30, last day only morning

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)

Evaluation of learning

A final exam will be conducted during the training.

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