UE Machine learning fundamentals

User information

Please note that you are curently looking at the ongoing Academic Programs. Applications are now closed for this academic year (2020-2021) for licences, professional licences, masters, DUT and regulated health training. If you are interested for an application in 2021-2022, please click on this link for the appropriate Academic Programs.

Degrees incorporating this pedagocial element :


  • Consistency of the Empirical Risk Minimization
  • Uniform Generalization Bounds and Structural Risk Minimization
  • Unconstrained Convex Optimization
  • Binary Classification algorithms (Perceptron, Adaboost, Logistic Regression, SVM) and their link with the ERM and the SRM principles
  • Multiclass classification
  • Application and experimentations

Evaluation : Homeworks (30%), Final exam (70%)

Recommended prerequisite

Statistics and probability (BSc)

Targeted skills

Understanding of fundamental notions in Machine Learning (inference, ERM and SRM principles, generalization bounds, classical learning models, unsupervised learning, semi-supervised learning.