Data mining has become an integral part of the business of

R&D. However, understanding the issues and the state of the art in the field requires a solid mathematical background.

Moyens techniques

Course material projected during training and sent to all trainees at the end of the course; case studies and practical examples chosen according to trainees' areas of interest

Suivi de l’exécution

All trainees are asked to sign in every half-day Evaluation: Questionnaire to assess skills acquired at the end of the course

Appréciation des résultats

Post-training satisfaction questionnaire

Objectifs pédagogiques

Firmly focused on what is needed, this training course aims to provide the theoretical foundations required to understand and apply recent advances in learning.

Technologies abordées

Bayes formula, model selection, maximum entropy, maximum likelihood, loss function, Stochastic Gradient Descent, regression, classification, linear algebra, validation

Compétences visées

  • Bayesian probability
  • Learning theory
  • Convex and stochastic optimization
  • Deep applications