Teaching code

4S009001

Credits

9

Coordinator

Vittorio Murino

Language

English

Scientific Disciplinary Sector (SSD)

ING-INF/05 - INFORMATION PROCESSING SYSTEMS

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

Basic Math
Probability and Statistics
Image Processing (optional)

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

Didactic methods

UL: Foundation of Machine Learning - Theory
Frontal lessons in the classroom and in computer classrooms for lab lectures
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UL: Foundation of Machine Learning - Laboratorio
Laboratory experiences, exercises.
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UL: Deep Learning - Teoria
Theory lessons will take place in the classroom with slide projection, while the laboratory lessons will be on the computer in the computer room and will consist in the development of some of the algorithms developed in class.
The Laboratory lessons will be aimed at developing practical examples of some of the topics described in the Theory part of the course. The lessons will take place in a computer laboratory in Phyton language.
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UL: Deep Learning - Laboratorio
Theory lessons will take place in the classroom with slide projection, while the laboratory lessons will be on the computer in the computer room and will consist in the development of some of the algorithms developed in class.
The Laboratory lessons will be aimed at developing practical examples of some of the topics described in the Theory part of the course. The lessons will take place in a computer laboratory in Phyton language.

Learning assessment procedures

Project development, with a technical report and oral presentation.
The projects should be performed with 1 or 2 persons.
3 persons are acceptable only in exceptional cases and for complex topics; in any case they should be agreed with the teacher.
The project presentation will include some questions aimed at assessing the knowledge of the contents of the course.

Evaluation criteria

UL: Foundation of Machine Learning - Theory
Theoretical and applied knowledge of the techniques taught in the course; critical ability to select techniques based on the problem; ability to use the techniques taught in the course.
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UL: Foundation of Machine Learning - Laboratorio
Applied knowledge of the techniques taught in the course; critical ability to select techniques based on the problem; ability to use the techniques learned during the laboratory.
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UL: Deep Learning - Theory
To pass the exam, students must demonstrate that they:
- have understood the theoretical principles and algorithms underlying the Machine Learning, Deep 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.
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UL: Deep Learning - Lab
To pass the exam, students must demonstrate that they:
- have understood the theoretical principles and algorithms underlying the Machine Learning, Deep 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.

Criteria for the composition of the final grade

Composition of the project grade and the grade on the theory questions

Exam language

Inglese