Studying at the University of Verona

Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.

A.A. 2020/2021

Academic calendar

The academic calendar shows the deadlines and scheduled events that are relevant to students, teaching and technical-administrative staff of the University. Public holidays and University closures are also indicated. The academic year normally begins on 1 October each year and ends on 30 September of the following year.

Academic calendar

Course calendar

The Academic Calendar sets out the degree programme lecture and exam timetables, as well as the relevant university closure dates..

Definition of lesson periods
Period From To
I semestre Oct 1, 2020 Jan 29, 2021
II semestre Mar 1, 2021 Jun 11, 2021
Exam sessions
Session From To
Sessione invernale d'esame Feb 1, 2021 Feb 26, 2021
Sessione estiva d'esame Jun 14, 2021 Jul 30, 2021
Sessione autunnale d'esame Sep 1, 2021 Sep 30, 2021
Degree sessions
Session From To
Sessione Estiva Jul 15, 2021 Jul 15, 2021
Sessione Autunnale Oct 15, 2021 Oct 15, 2021
Sessione Invernale Mar 15, 2022 Mar 15, 2022
Holidays
Period From To
Festa dell'Immacolata Dec 8, 2020 Dec 8, 2020
Vacanze Natalizie Dec 24, 2020 Jan 3, 2021
Epifania Jan 6, 2021 Jan 6, 2021
Vacanze Pasquali Apr 2, 2021 Apr 5, 2021
Santo Patrono May 21, 2021 May 21, 2021
Festa della Repubblica Jun 2, 2021 Jun 2, 2021

Exam calendar

Exam dates and rounds are managed by the relevant Science and Engineering Teaching and Student Services Unit.
To view all the exam sessions available, please use the Exam dashboard on ESSE3.
If you forgot your login details or have problems logging in, please contact the relevant IT HelpDesk, or check the login details recovery web page.

Exam calendar

Should you have any doubts or questions, please check the Enrolment FAQs

Academic staff

B C D F G M P Q R S V

Baruffi Maria Caterina

mariacaterina.baruffi@univr.it

Belussi Alberto

alberto.belussi@univr.it +39 045 802 7980

Bombieri Nicola

nicola.bombieri@univr.it +39 045 802 7094

Calanca Andrea

andrea.calanca@univr.it +39 045 802 7847

Castellani Umberto

umberto.castellani@univr.it +39 045 802 7988

Ceccato Mariano

mariano.ceccato@univr.it

Chiarini Andrea

andrea.chiarini@univr.it 045 802 8223

Cristani Marco

marco.cristani@univr.it +39 045 802 7841

Cubico Serena

serena.cubico@univr.it 045 802 8132

Dall'Alba Diego

diego.dallalba@univr.it +39 045 802 7074

Farinelli Alessandro

alessandro.farinelli@univr.it +39 045 802 7842

Favretto Giuseppe

giuseppe.favretto@univr.it +39 045 802 8749 - 8748

Fiorini Paolo

paolo.fiorini@univr.it 045 802 7963

Fummi Franco

franco.fummi@univr.it 045 802 7994

Giachetti Andrea

andrea.giachetti@univr.it +39 045 8027998

Giacobazzi Roberto

roberto.giacobazzi@univr.it +39 045 802 7995

Maris Bogdan Mihai

bogdan.maris@univr.it +39 045 802 7074

Melzi Simone

simone.melzi@univr.it +39 045 802 7068

Muradore Riccardo

riccardo.muradore@univr.it +39 045 802 7835

Murino Vittorio

vittorio.murino@univr.it 045 802 7996

Muscolo Giovanni Gerardo

giovannigerardo.muscolo@univr.it

Pravadelli Graziano

graziano.pravadelli@univr.it +39 045 802 7081

Quaglia Davide

davide.quaglia@univr.it +39 045 802 7811

Quintarelli Elisa

elisa.quintarelli@univr.it +39 045 802 7852

Romeo Alessandro

alessandro.romeo@univr.it +39 045 802 7974-7936; Lab: +39 045 802 7808

Setti Francesco

francesco.setti@univr.it +39 045 802 7804

Spoto Nicola Fausto

fausto.spoto@univr.it +39 045 8027940

Villa Tiziano

tiziano.villa@univr.it +39 045 802 7034

Study Plan

The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University. Please select your Study Plan based on your enrolment year.

ModulesCreditsTAFSSD
9
B
(ING-INF/04)
Compulsory courses for Embedded & IoT Systems
Compulsory courses for Robotics systems
6
B/C
(INF/01)
6
B/C
(ING-INF/05)
Compulsory courses for Smart systems &data analytics
6
B/C
(ING-INF/05)
6
B/C
(INF/01 ,ING-INF/06)
ModulesCreditsTAFSSD
Compulsory courses for Embedded & IoT Systems
Compulsory courses for Robotics systems
6
B/C
(ING-INF/04)
Compulsory courses for Smart systems &data analytics
6
B/C
(ING-INF/05)
Final exam
24
E
-

1° Year

ModulesCreditsTAFSSD
9
B
(ING-INF/04)
Compulsory courses for Embedded & IoT Systems
Compulsory courses for Robotics systems
6
B/C
(INF/01)
6
B/C
(ING-INF/05)
Compulsory courses for Smart systems &data analytics
6
B/C
(ING-INF/05)
6
B/C
(INF/01 ,ING-INF/06)

2° Year

ModulesCreditsTAFSSD
Compulsory courses for Embedded & IoT Systems
Compulsory courses for Robotics systems
6
B/C
(ING-INF/04)
Compulsory courses for Smart systems &data analytics
6
B/C
(ING-INF/05)
Final exam
24
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°3 courses to be chosen among the following
6
C
(INF/01)
6
C
(ING-IND/16)
6
C
(INF/01)
6
C
(INF/01 ,ING-IND/34)
6
C
(SECS-P/10)
Between the years: 1°- 2°
Between the years: 1°- 2°
Other activities
3
F
-
Between the years: 1°- 2°
Training
3
F
-

Legend | Type of training activity (TTA)

TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.




SPlacements in companies, public or private institutions and professional associations

Teaching code

4S009001

Credits

9

Coordinatore

Vittorio Murino

Scientific Disciplinary Sector (SSD)

ING-INF/05 - INFORMATION PROCESSING SYSTEMS

Language

English

The teaching is organized as follows:

Teoria

Credits

7

Period

II semestre

Academic staff

Vittorio Murino

Laboratorio

Credits

2

Period

II semestre

Academic staff

Vittorio Murino

Learning outcomes

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.

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.

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

Examination Methods

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 course contents.

Type D and Type F activities

Le attività formative in ambito D o F comprendono gli insegnamenti impartiti presso l'Università di Verona o periodi di stage/tirocinio professionale.
Nella scelta delle attività di tipo D, gli studenti dovranno tener presente che in sede di approvazione si terrà conto della coerenza delle loro scelte con il progetto formativo del loro piano di studio e dell'adeguatezza delle motivazioni eventualmente fornite.

 

I semestre From 10/1/20 To 1/29/21
years Modules TAF Teacher
1° 2° Matlab-Simulink programming D Bogdan Mihai Maris (Coordinatore)
II semestre From 3/1/21 To 6/11/21
years Modules TAF Teacher
1° 2° Introduction to 3D printing D Franco Fummi (Coordinatore)
1° 2° Python programming language D Vittoria Cozza (Coordinatore)
1° 2° HW components design on FPGA D Franco Fummi (Coordinatore)
1° 2° Rapid prototyping on Arduino D Franco Fummi (Coordinatore)
1° 2° Protection of intangible assets (SW and invention)between industrial law and copyright D Roberto Giacobazzi (Coordinatore)
List of courses with unassigned period
years Modules TAF Teacher
1° 2° The fashion lab (1 ECTS) D Maria Caterina Baruffi (Coordinatore)
1° 2° The course provides an introduction to blockchain technology. It focuses on the technology behind Bitcoin, Ethereum, Tendermint and Hotmoka. D Nicola Fausto Spoto (Coordinatore)

Career prospects


Module/Programme news

News for students

There you will find information, resources and services useful during your time at the University (Student’s exam record, your study plan on ESSE3, Distance Learning courses, university email account, office forms, administrative procedures, etc.). You can log into MyUnivr with your GIA login details.

Attendance

As stated in point 25 of the Teaching Regulations for the A.Y. 2021/2022, attendance at the course of study is not mandatory.
Please refer to the Crisis Unit's latest updates for the mode of teaching.

Graduation

List of theses and work experience proposals

theses proposals Research area
Domain Adaptation Computer Science and Informatics: Informatics and information systems, computer science, scientific computing, intelligent systems - Computer graphics, computer vision, multi media, computer games
Domain Adaptation Computer Science and Informatics: Informatics and information systems, computer science, scientific computing, intelligent systems - Machine learning, statistical data processing and applications using signal processing (e.g. speech, image, video)
Domain Adaptation Computing Methodologies - IMAGE PROCESSING AND COMPUTER VISION
Domain Adaptation Computing methodologies - Machine learning

Gestione carriere


Further services

I servizi e le attività di orientamento sono pensati per fornire alle future matricole gli strumenti e le informazioni che consentano loro di compiere una scelta consapevole del corso di studi universitario.