Learning outcomes

At the end of the course, the student must demonstrate an understanding of the different topics covered (see content), i.e., be able to express in his own words the theory and methods seen in the course and explain in which context they are useful. He must also be able to implement the techniques seen during a complex data analysis problem.

Content

As an extension of the IDASM102 "Machine learning and data mining" course, this course explores more advanced methods in machine learning and deep learning.  The following topics will be discussed:

  1. probabilistic learning
  2. deep learning for images
  3. XAI and regularisation
  4. XGBoost and feature importance
  5. deep mearning for sequences
  6. density estimation and information theory
  7. generative deep learning models
  8. from text mining to large language models
  9. machine learning in the cloud and MLOps

These courses will be complemented by two "research talks" sessions based on live interventions by scientific experts and international conferences recorded and viewed during the lesson.  A session will also be devoted to the presentation of projects (see "evaluation mode").

Language of instruction

French
Training Study programme Block Credits Mandatory
Master in Computer Science Standard 1 5 No
Master in Computer Science Finalité spécialisée en data science 2 5 No
Master in Computer Science Finalité spécialisée en software engineering 2 5 No