Data mining has become an integral part of R&D activities.

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 course aims to provide the theoretical foundations required to understand and apply recent advances in machine learning.

Technologies abordées

Ipython notebook, git, PyTorch, TensorFlow, CPU vs GPU, numpy, Pandas, matplotlib, scikit-learn, bokeh

Compétences visées

  • Syntax, flow controls
  • Object programming, inheritance
  • Data visualization