Mathematical Foundations of Machine Learning
Credits
6 ECTS, 36h
Instructors
Massih-Rezah Amini and Emilie Devijver
Syllabus
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