Teaching code

4S009001

Credits

9

Coordinator

Vittorio Murino

Language

English

Scientific Disciplinary Sector (SSD)

ING-INF/05 - INFORMATION PROCESSING SYSTEMS

Courses Single

Authorized

Moodle Seminars 0

The teaching is organized as follows:

Learning objectives

The course aims to provide the theoretical foundations and describe the main methodologies related to Machine Learning and Pattern Recognition and, more generally, to Artificial Intelligence. In particular, the course will deal with the methods of analysis, recognition and automatic classification of data of any type, typically called patterns. These disciplines are at the basis, are used, and often complement many other disciplines and application areas of wide diffusion, such as computational vision, robotics, image processing, data mining, analysis and interpretation of medical and biological data, bioinformatics, biometrics, video surveillance, speech and text recognition, and many others. More precisely, the methodologies that will be introduced in the course are often an integral part of the aforementioned application areas, and constitute their intelligent part with the ultimate goal of understanding (classifying, recognizing, analyzing) the data from the process of interest (whether they are signals, images, strings, categorical, or other types of data). Starting from the type of measured data, the entire analysis pipeline will be considered such as the extraction and selection of characteristics (features); supervised and unsupervised learning methods, parametric and non-parametric analysis techniques, and validation protocols. Finally, the recent deep learning techniques will be analyzed in general, providing basic notions, and addressing open problems with some case studies. In conclusion, the course aims to provide the students with a set of theoretical foundations and algorithmic tools to address the problems that can be encountered in strategic and innovative industrial sectors such as those involving robotics, cyber physical systems, (big) data mining, digital manufacturing, visual inspection of products/production processes, and automation in general.

Prerequisites and basic notions

The prerequisites for following the course consist in having acquired skills in Probability and Statistics. Knowledge of issues related to image processing is also essential for understanding the course in question.

Program

The course aims at providing the theoretical foundations and main methods related to the analysis of data, not necessarily images, in short, theory and statistical classification methods will be discussed.
These themes are preparatory to the most recent Deep Learning techniques, which will be intoduced in the final part of the course.
Course content
Introduction: what it is, what it is used for, systems, applications
Bayes' decision theory
Estimation of parameters and nonparametric methods
Linear, nonlinear classifiers and discriminant functions
Linear transformations and Fisher method, feature extraction and selection, Principal Component Analysis
Gaussian mixtures and Expectation-Maximization algorithm
Kernel Methods and Support Vector Machines
Hidden Markov Models
Artificial neural networks
Unsupervised classification & clustering
Classifier ensembles
Deep learning fundamentals
Deep learning advanced topics

Bibliography

Didactic methods

The Theory lessons will take place in the classroom with slide projection, while the Laboratory lessons will be carried out on the computer in the computer room. The latter will consist in the development of some of the algorithms explained during the Theory lectures.

Learning assessment procedures

To pass the exam, students must demonstrate that they:
- have understood the theoretical principles and algorithms underlying the Machine Learning & Artificial Intelligence techniques described in class;
- be able to present one's arguments in a precise, organic and structured way, without digressions;
- knowing how to apply the acquired knowledge to solve application problems presented in the form of exercises, questions or projects.
The exam will consist in the development of a project (2 people max, 3 people for more complex projects, to be agreed with the teacher), followed by the writing of a technical report and an oral presentation.
During the oral presentation of the project, questions will be asked about the contents of the course described in class, aimed at assessing the knowledge of the contents of the course.

Exam language

Inglese