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.

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..

Academic year:
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 Enrollment FAQs

Academic staff

B C D F G M P Q R S V

Baruffi Maria Caterina

symbol email mariacaterina.baruffi@univr.it

Belussi Alberto

symbol email alberto.belussi@univr.it symbol phone-number +39 045 802 7980

Bombieri Nicola

symbol email nicola.bombieri@univr.it symbol phone-number +39 045 802 7094

Calanca Andrea

symbol email andrea.calanca@univr.it symbol phone-number +39 045 802 7847

Castellani Umberto

symbol email umberto.castellani@univr.it symbol phone-number +39 045 802 7988

Ceccato Mariano

symbol email mariano.ceccato@univr.it

Chiarini Andrea

symbol email andrea.chiarini@univr.it symbol phone-number 045 802 8223

Cozza Vittoria

symbol email vittoria.cozza@univr.it

Cristani Marco

symbol email marco.cristani@univr.it symbol phone-number +39 045 802 7841

Cubico Serena

symbol email serena.cubico@univr.it symbol phone-number 045 802 8132

Dall'Alba Diego

symbol email diego.dallalba@univr.it symbol phone-number +39 045 802 7074

Farinelli Alessandro

symbol email alessandro.farinelli@univr.it symbol phone-number +39 045 802 7842

Favretto Giuseppe

symbol email giuseppe.favretto@univr.it symbol phone-number +39 045 802 8749 - 8748

Fiorini Paolo

symbol email paolo.fiorini@univr.it symbol phone-number 045 802 7963

Fummi Franco

symbol email franco.fummi@univr.it symbol phone-number 045 802 7994

Giachetti Andrea

symbol email andrea.giachetti@univr.it symbol phone-number +39 045 8027998

Giacobazzi Roberto

symbol email roberto.giacobazzi@univr.it symbol phone-number +39 045 802 7995

Gialanella Stefano

symbol email stefano.gialanella@unitn.it symbol phone-number 0461 282420

Maris Bogdan Mihai

symbol email bogdan.maris@univr.it symbol phone-number +39 045 802 7074

Melzi Simone

symbol email simone.melzi@univr.it symbol phone-number +39 045 802 7068

Muradore Riccardo

symbol email riccardo.muradore@univr.it symbol phone-number +39 045 802 7835

Murino Vittorio

symbol email vittorio.murino@univr.it symbol phone-number +39 045 802 7996

Muscolo Giovanni Gerardo

symbol email giovannigerardo.muscolo@univr.it

Pravadelli Graziano

symbol email graziano.pravadelli@univr.it symbol phone-number +39 045 802 7081

Quaglia Davide

symbol email davide.quaglia@univr.it symbol phone-number +39 045 802 7811

Quintarelli Elisa

symbol email elisa.quintarelli@univr.it symbol phone-number +39 045 802 7852

Romeo Alessandro

symbol email alessandro.romeo@univr.it symbol phone-number +39 045 802 7936; Lab: +39 045 802 7808

Setti Francesco

symbol email francesco.setti@univr.it symbol phone-number +39 045 802 7804

Spoto Nicola Fausto

symbol email fausto.spoto@univr.it symbol phone-number +39 045 802 7940

Villa Tiziano

symbol email tiziano.villa@univr.it symbol phone-number +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 enrollment year.

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
INF/01 ,ING-INF/06
6
B/C
ING-INF/05

2° Year  activated in the A.Y. 2021/2022

ModulesCreditsTAFSSD
Compulsory courses for Embedded & IoT Systems
Compulsory courses for Robotics systems
Compulsory courses for Smart systems &data analytics
6
B/C
ING-INF/05
Final exam
24
E
-
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
INF/01 ,ING-INF/06
6
B/C
ING-INF/05
activated in the A.Y. 2021/2022
ModulesCreditsTAFSSD
Compulsory courses for Embedded & IoT Systems
Compulsory courses for Robotics systems
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°
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.




S Placements in companies, public or private institutions and professional associations

Teaching code

4S009001

Credits

9

Coordinator

Vittorio Murino

Language

English en

Scientific Disciplinary Sector (SSD)

ING-INF/05 - INFORMATION PROCESSING SYSTEMS

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.

Students with disabilities or specific learning disorders (SLD), who intend to request the adaptation of the exam, must follow the instructions given HERE

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.

 

Academic year:
I semestre From 10/1/20 To 1/29/21
years Modules TAF Teacher
1° 2° Matlab-Simulink programming D Bogdan Mihai Maris (Coordinator)
II semestre From 3/1/21 To 6/11/21
years Modules TAF Teacher
1° 2° Introduction to 3D printing D Franco Fummi (Coordinator)
1° 2° Python programming language D Vittoria Cozza (Coordinator)
1° 2° HW components design on FPGA D Franco Fummi (Coordinator)
1° 2° Rapid prototyping on Arduino D Franco Fummi (Coordinator)
1° 2° Protection of intangible assets (SW and invention)between industrial law and copyright D Roberto Giacobazzi (Coordinator)
List of courses with unassigned period
years Modules TAF Teacher
1° 2° The fashion lab (1 ECTS) D Maria Caterina Baruffi (Coordinator)
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 (Coordinator)

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: only in this way will you be able to receive notification of all the notices from your teachers and your secretariat via email and also via the Univr app.

Graduation

Deadlines and administrative fulfilments

For deadlines, administrative fulfilments and notices on graduation sessions, please refer to the Graduation Sessions - Science and Engineering service.

Need to activate a thesis internship

For thesis-related internships, it is not always necessary to activate an internship through the Internship Office. For further information, please consult the dedicated document, which can be found in the 'Documents' section of the Internships and work orientation - Science e Engineering service.

Final examination regulations

Upon completion of the Master’s degree dissertation, students are awarded 24 CFU, which equates to no more than 4-5 months of full-time work.

Goals
The primary goal of a dissertation is to develop an original study that may include an application project or a theoretical topic related to specific design issues, or a critical review of the most recent developments in a given field of study. During the preparation of the dissertation, under the guidance of the Supervisor and co-supervisors (if any), the student is expected to conduct an in- depth study of the chosen topic, while gaining the ability to summarise and creatively apply the knowledge acquired. The dissertation should focus on topics of engineering and computer science, or closely related areas of study. The work shall consist in the written presentation of activities that may be structured as follows:

  1. design and development of applications or systems;
  2. critical analysis of contributions from the scientific literature;
  3. original research contributions.

The dissertation shall be written in English and can be presented either in English or in Italian, also relying on multimedia tools such as presentations and videos.

Assessment methods and examination procedures
Each dissertation can be either internal or external, depending on whether it is carried out at the University of Verona or in collaboration with another institution. For each dissertation a Supervisor, one or more co-supervisors (optional) and an Examiner will be appointed. The Examiner is appointed by the Computer Science Teaching Committee at least 20 days before the presentation of the dissertation, once the student's eligibility to take the Master's degree examination has been verified. With regard to the legal aspects related to the dissertation and its scientific outcomes (e.g. intellectual property of research outcomes), please refer to the relevant legislation and the University Regulations.
Evaluation of the dissertation
The Supervisor, the co-supervisor/s (if any) and the Examiner will evaluate the dissertation based on the following criteria:

  1. level of in-depth analysis carried out, in relation to the most recent developments in the areas related to information technology, with a focus on medical and biological applications;
  2. scientific and/or technological outcomes of the dissertation;
  3. student’s critical thinking;
  4. student’s experimental and/or formal development;
  5. student’s ability to carry out independent work (this point will not be assessed by the Examiner);
  6. value of the methodologies used;
  7. accuracy in planning and writing the dissertation.

Graduation mark
The graduation mark (based on a 110-point scale) is a whole value between 66/110 and 110/110 and is calculated by adding together the following elements (then rounding the result to the nearest whole number, e.g. 93.50 => 94; 86.49 => 86):

  • 1) the average of the marks gained in the modules, weighted according to CFU, converted to a 110-point scale;
  • 2) evaluation of the dissertation and the oral presentation during the final examination, based on the following methods:
    • a) each of the points 1-7 listed above will be assigned a coefficient between 0 and 1 (fractional coefficient with one decimal place);
    • b) the quality of the presentation will be assessed by awarding a coefficient between 0 and 1 (fractional coefficient with one decimal place);
    • c) the sum of the points resulting from (a) and (b).

The Graduation Committee may award one extra point in the following cases: cum laude honours obtained in the exams taken during the degree programme; participation in internships officially recognised by the Computer Science Teaching Committee; taking extra modules; and the achievement of the degree in a time that is shorter than the normal duration of the degree programme. If the final score is 110/110, the Graduation Committee may award cum laude honours by unanimous decision.

External dissertations
An external dissertation is a work carried out in collaboration with an institution/body other than the University of Verona. In this case, the topic of the dissertation must be agreed in advance with a Supervisor from the University of Verona. In addition, the student must indicate at least one co- supervisor belonging to the external institution/body, who will support the student during the work on the dissertation. The Supervisor and the co- supervisors must be indicated in the online graduation application. The insurance aspects relating to the student's stay at the external institution are regulated by the regulations in force at the University of Verona. If the dissertation involves a period of training at the external institution/body, then it is necessary that the University of Verona enters into a specific agreement with such institution/body. The scientific outcomes of the dissertation will be available to all parties involved. In particular, the contents and results of the dissertation are to be considered public. For all matters not strictly scientific (e.g. agreements, insurance) the resolution of the Academic Senate of 12 January 1999 shall be taken as a reference.

Supervisor, co-supervisors, examiners
The dissertation presentation is introduced by the Supervisor. Professors belonging to the Department of Computer Science may be appointed as Supervisors, as well as any external lecturers who are deemed suitable by the Teaching Committee based on their area of study and level of expertise.
In addition to those who have the above requirements to be appointed as Supervisor, the following individuals may be appointed as co-supervisors: researchers working in external research institutes, research grant holders, post-doctoral fellowship holders, PhD students, technical staff of the Department, external experts appointed by an Italian University, corporate officers who have a remarkable experience in the field relevant to the topic of the dissertation. Examiners may be appointed among lecturers and researchers at the Department of Computer Science of the University of Verona, who are deemed to be experts in the specific field of the dissertation topic.

Graduation Committee
The Graduation Committee shall include five members, of which at least four are professors in the Master's degree programme in Computer Engineering for Robotics and Smart Industry. Based on the number of graduates, the Computer Science Teaching Committee will identify the most appropriate organisational methods for administering the examination, and it shall make available the calendar of tests at least one week before the examination itself. The procedures and deadlines for the submission of the graduation application are established by the Computer Science Teaching Committee and by the relevant offices.

List of thesis 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

Career management


Student login and resources


Erasmus+ and other experiences abroad


Attendance modes and value