SD-TSIA-211: Optimization for Machine Learning

Many statistical learning problems (calculating an estimator, a classifier, etc.) boil down to the minimization of a functional, typically an empirical risk. Optimization methods are therefore central to the “practical” part of statistical learning. In this module, the students will discover not only the theoretical foundations that are a continuation of the optimization course followed in first semester, but also different techniques to deal specifically with the case of massive data.