Teaching unit 1: Pre-course on imaging and learning
Blended
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. The theoretical discussions will be complemented by numerical implementations using Python, leveraging the libraries NumPy, Matplotlib, and PyTorch.
Lecturers
Luca Ratti
MAT University of Bologna