Numerical Analysis Laboratory

The course aims to provide the theoretical and implementation foundations of Machine Learning, with particular reference to modern deep neural networks (Deep Learning). Notions of approximation theory, optimization theory and statistical theory of learning related to neural networks will be provided. The link of neural network-based algorithms with classical numerical methods and their properties will be emphasized. During the course, some significant applications will also be presented, relating in particular to image and signal processing.

At the end of the course students will possess basic knowledge relating to the structure of modern deep neural networks, they will be able to recognize the fundamental typologies and will possess knowledge of the main training algorithms. They will be able to understand the approximation properties of the networks in question.
Students will also be able to implement some relevant types of neural networks in Python/Pytorch in their completeness