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 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 Pattern Recognition, a class of automatic methodo-logies 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 representa-tion, classification, clustering 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). Af-ter attending the course, the students will be able to analyse a biological problem from a Pattern Recognition perspective; they will also have the skills needed to invent, develop and implement the dif-ferent components of a Pattern Recognition System. Module2 (Signal and image processing 1): The course aims at providing the students with the fundamentals of signal and image processing with the emphasis on aspects that are relevant in the field of bioinformatics at both theoretical and practical levels. At the end of the course the students will be able to analyze the typical signal and image processing problems encountered in bioinformatics as well as to devise and implement suitable solutions grounding on the knowledge gained in the theory sessions and using the main available toolboxes.