Learning objectives

The aim of the course is to provide the theoretical and practical foundations of data processing and modeling in the field of bioinformatics, with particular emphasis on signal and image processing and supervised pattern recognition. The course comprises two modules as detailed below. Module1: Pattern Recognition. This module is aimed at providing the theoretical and applicative bases of Supervised Pattern Recognition, a class of automatic methodologies used to recognize and recover information from biological data. In particular, during the course the main techniques of this area will be presented and discussed, in particular linked to representation, classification, and validation. The focus is more on the description of the employed methodologies rather than on the details of applicative programs (already seen in other courses). After attending the course, the students will be able to analyse a biological problem from a Supervised Pattern Recognition perspective; they will also have the skills needed to invent, develop and implement the different components of a supervised Pattern Recognition System. Module 2: Signal and Image Processing. The module aims to provide students with the basic notions of biomedical signals and images, from the underlaying principles of data formation and acquisition to the fundamental concepts and main tools required for processing them. At the end of the course, students will be able to recognize and address the most common problems arising while processing biomedical data, in particular in the context of diagnostic imaging, as well as to apply the acquired methodologies and the main software available (during hands-on sessions in Python).