Mathematical Foundations of Machine Learning


6 ECTS, 36h


Massih-Rezah Amini and Emilie Devijver


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

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