Introduction to machine learning

This module introduces the basic concepts and tools of machine learning, whose aim is to extract information from data of any kind (signal, image, etc.), with or without an underlying mathematical model. This module requires some basic knowledge of matrix calculus and probability.

Probability basics

Reminders of probability
Statistics

statistical model, observations
estimators, risk, max likelihood
asymptotic performance


Machine learning

supervised learning

regression (linear, ridge/LASSO, nonlinear)
classification (kNN, LDA, SVM)


dimension reduction (PCA)
unsupervised learning (clustering, k-means)