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.

Technical resources

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

Performance monitoring

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

Assessment of results

Post-training satisfaction questionnaire

Pedagogical objectives

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 covered

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

Target skills

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