Informazioni sul corso

Objectives

This course offers an introduction to the foundational mathematical aspects, widely used computational tools and scientific applications of modern machine learning (ML).
At the end of the course the student will have a working knowledge on several topics of supervised and unsupervised ML, ranging from the well-established theory of Gaussian process regressions, to neural networks and deep-learning, to convolutional neural networks for image classification.

Prerequisites

Linear algebra. Calculus. Basic knowledge of statistics and statistical mechanics. Basic knowledge of simple Unix shell commands and Python is not a prerequisite but is suggested.