Teaching code

4S02803

Credits

6

Coordinatore

Vittorio Murino

Language

Italian

Scientific Disciplinary Sector (SSD)

ING-INF/05 - INFORMATION PROCESSING SYSTEMS

Moodle Seminars

The teaching is organized as follows:

Learning outcomes

The course aims to provide: i) methodological principles underlying the classification; ii) feature selection and extraction techniques; iii) algorithms for supervised and unsupervised learning; parametric and non-parametric parameter estimation; iv) cross-validation techniques for the validation of classifiers. At the end of the course the student should be able to understand if a classification problem can be solved with some existing technology and, in that case, the type of machine learning algorithm that has to be used for the training. Furthermore, the student must demonstrate: i) to understand what kind of characteristics or patterns should be extracted from the raw data coming from a sensor; ii) to understand what kind of classifier should be used in relation with the encountered problem: iii) to understand the complexity of the recognition problem in computational terms; iv) to produce software that recognizes real data; v) be able to use other people's code and modify it adapting it to the problem under examination. This knowledge will allow the student to understand: i) that fit measures guarantee an effective classifier after the phase of his training; ii) what are the techniques for validating the results of a classifier. At the end of the course the student will be able to understand a machine learning or pattern recognition paper.

Program

Introduction: what, what it serves for, systens and applications
Bayes decision theory
Parameter estimation and non-parametric methods
Linear and nonlinear classifiers and discriminant functions
Linear transformations, Fisher method, feature estraction and selection, Principal Component Analysis
Gaussian mixtures and Expectation-Maximization
Kernel methods and Support Vector Machines
Artificial neural networks
Unsupervised learning and clustering
Hidden Markov Models

Examination Methods

Project with report and oral presentation
To be executed with 2 people, 3 people max for more complex projects (to agree with professor)
Oral presentation will also be focused on verifying the knowledge of the course contents