Teaching Unit 1: Pre-course on imaging and learning

Lecturers:  Prof.  Luca Calatroni  and  Luca Ratti

Basic tools for theoretical understanding and practical use of the main supervised and unsupervised learning algorithms.

This teaching unit offers a general introduction to the core topics of the school, together with a short review of key preliminary concepts relevant to the subsequent courses. The focus will be on the task of image denoising, approached from both a theoretical and a computational standpoint. We will begin by formulating image denoising as a Bayesian inverse problem, highlighting connections to variational regularization theory and basic optimization methods. Then, we will reinterpret the problem within a statistical learning framework, demonstrating the effectiveness of end-to-end approaches based on neural networks with appropriate architectures.