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..

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
Holidays
Period From To
Festa dell'Immacolata Dec 8, 2020 Dec 8, 2020
Vacanze Natalizie Dec 24, 2020 Jan 3, 2021
Vacanze Pasquali Apr 2, 2021 Apr 5, 2021
Festa del Santo Patrono May 21, 2021 May 21, 2021
Festa della Repubblica Jun 2, 2021 Jun 2, 2021
Vacanze estive Aug 9, 2021 Aug 15, 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

A B C D F G I M O P Q R S T V Z

Albi Giacomo

symbol email giacomo.albi@univr.it symbol phone-number +39 045 802 7913

Badino Massimiliano

symbol email massimiliano.badino@univr.it symbol phone-number +39 045 802 8459

Bazzani Claudia

symbol email claudia.bazzani@univr.it symbol phone-number 0458028734

Begalli Diego

symbol email diego.begalli@univr.it symbol phone-number 045 8028731

Boscolo Galazzo Ilaria

symbol email ilaria.boscologalazzo@univr.it symbol phone-number +39 045 8127804

Carra Damiano

symbol email damiano.carra@univr.it symbol phone-number +39 045 802 7059

Carradore Marco

symbol email marco.carradore@univr.it

Castellini Alberto

symbol email alberto.castellini@univr.it symbol phone-number +39 045 802 7908

Ceccato Mariano

symbol email mariano.ceccato@univr.it

Chiarini Andrea

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

Cobelli Nicola

symbol email nicola.cobelli@univr.it symbol phone-number 0458028295

Collet Francesca

symbol email francesca.collet@univr.it symbol phone-number +39 045 8027979

Confente Ilenia

symbol email ilenia.confente@univr.it symbol phone-number 045 802 8174

Dai Pra Paolo

symbol email paolo.daipra@univr.it symbol phone-number +39 0458027093

Dalla Preda Mila

symbol email mila.dallapreda@univr.it

D'Asaro Fabio Aurelio

symbol email fabioaurelio.dasaro@univr.it symbol phone-number 0458028431
Di NicolaAndrea

Di Nicola Andrea

Di Persio Luca

symbol email luca.dipersio@univr.it symbol phone-number +39 045 802 7968

Farinelli Alessandro

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

Gaudenzi Barbara

symbol email barbara.gaudenzi@univr.it symbol phone-number 045 802 8623

Giachetti Andrea

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

Guerra Giorgia

symbol email giorgia.guerra@univr.it

Marastoni Niccolo'

symbol email niccolo.marastoni@univr.it

Mola Lapo

symbol email lapo.mola@univr.it symbol phone-number 0458028565

Owusu Abigail

symbol email abigail.owusu@univr.it

Paci Federica Maria Francesca

symbol email federicamariafrancesca.paci@univr.it symbol phone-number +39 045 802 7909

Pelgreffi Igor

symbol email igor.pelgreffi@univr.it

Quintarelli Elisa

symbol email elisa.quintarelli@univr.it symbol phone-number +390458027852
Foto Alice,  November 22, 2017

Raffaele Alice

symbol email alice.raffaele@univr.it

Setti Francesco

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

Troiano Stefano

symbol email stefano.troiano@univr.it symbol phone-number +39 045 8425317

Vadala' Rosa Maria

symbol email rosamaria.vadala@univr.it

Zardini Alessandro

symbol email alessandro.zardini@univr.it symbol phone-number 045 802 8565

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

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

ModulesCreditsTAFSSD
Training
6
F
-
Final exam
22
E
-
activated in the A.Y. 2021/2022
ModulesCreditsTAFSSD
Training
6
F
-
Final exam
22
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°1 module among the following (1st year: Big Data epistemology and Social research; 2nd year: Cybercrime, Data protection in business organizations, Comparative and Transnational Law & Technology)
6
C
IUS/17
Between the years: 1°- 2°2 courses among the following (1st year: Business analytics, Digital Marketing and market research; 2nd year: Logistics, Operations & Supply Chain, Digital transformation and IT change, Statistical methods for Business intelligence)
Between the years: 1°- 2°2 courses among the following (1st year: Complex systems and social physics, Discrete Optimization and Decision Making, 2nd year: Statistical models for Data Science, Continuous Optimization for Data Science, Network science and econophysics, Marketing research for agrifood and natural resources)
Between the years: 1°- 2°2 courses among the following (1st year: Data Visualisation, Data Security & Privacy, Statistical learning, Mining Massive Dataset, 2nd year: Machine Learning for Data Science)
Between the years: 1°- 2°

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

4S009075

Credits

3

Language

English en

Scientific Disciplinary Sector (SSD)

M-FIL/03 - MORAL PHILOSOPHY

Period

Primo semestre, Secondo semestre

To show the organization of the course that includes this module, follow this link:  Course organization

Learning outcomes

Learning outcomes

The Ethics module aims to provide some basic tools of contemporary ethical reflection; to enable students to recognize an ethical dilemma in a public debate or in daily professional practice; to learn to examine and reformulate it on the basis of the reflection tools learned; to be able to critically elaborate an operational perspective in the face of dilemmas and controversial cases encountered. At the end of the course the student has to show to have acquired the following skills:
- ability to analyze the realm of the so-called documentality, within the horizon of a complex society in relation to the influence of the IT device, understood in its various forms (AI; robotics; infosphere; digital environments);
- knowledge and capability to interpret and know how to use the concepts of truth, device, objectification, subjectivation and responsibility, as well as the articulated interaction of the phenomena that these concepts allow to describe and direct.

Program

Course program


Introduction: Philosophy and technology. General issues of the man-machine and man-technique relationship. The digital society and the main ethical issues raised: robotics; infosphere; artificial intelligence (AI); algorithms.
The ethical framework of a “responsible data protection”.

A prime example of a moral dilemma and ethical question: self-driving cars.
Planning a decision: logical possibility and ethical limits. The MIT Moral Machine Experiment.

The different ethical approaches in the field in the problems of ethics applied to technologies.
Deontological Ethics. Utilitarian ethics. Ethics of virtue.

Guidelines for ethical AI and Roboethics: analysis of the main International Guidelines. Examination of concepts and keywords. Transparency. Justice and Fairness. Responsibility. Privacy. Freedom and Autonomy. Trust. Sustainability. Dignity. Solidarity.

The problem of the ethical relationship with robots and with the algorithms that program them.
Case studies: robot and work; robot and medicine; care robot; autonomous weapons and war scenarios. Other examples: neuroscience, neuroimaging, artificial neural networks, deep learning.

Data ontology.
Philosophical concept of data. Historical notes: writing, data recording, the social dimension of the data.

Social networks: processes and practices. Cyberspace, digital identity and personal identity.
Case study: “we are our data”? Ethical problems of digital subjectivation.
Ethics of data storage. Ethics of management and data transfer.

Algorithms. Ethical and political problems.
The ethical question of automatism from a general and multidisciplinary point of view.
The automatic society: Turing; Wiener; Pollock; Anders; Stiegler. Dilemmas related to automation, cybernetics, lifelong education as a remedy.

Data processing algorithms and Machine Learning. The fundamental sequence: understanding data, prediction, decision making. The problem of technological unemployment. Replacement of the human.

The perspective of literature and art. A few examples. Isaac Asimov and Asimov’s three laws of robotics. Kazuo Ishiguro and the novel Klara and the Sun. The case of the android in the film Blade Runner. Generalization to the field of Roboethics and Machine Ethics.

New technologies and dating: philosophical problems that remain open. Future scenarios.

Bibliography

Visualizza la bibliografia con Leganto, strumento che il Sistema Bibliotecario mette a disposizione per recuperare i testi in programma d'esame in modo semplice e innovativo.

Examination Methods

Final Exam (Ethics Module)

The final exam (for the Ethics module) consists of an oral interview, in English, on the main topics of the course.

Alternatively, the student can carry out a written short paper, in English, on the topics of the course or on similar issues. In this examination mode, the oral exam will consist only in the critical discussion of one’s own paper.
For those who decide to bring the written paper, it is mandatory to contact the teacher, who will provide all the necessary information, and to agree with him on the method and topic. The final delivery must take place before the date of the exam session.
The structure of the short paper must be as follows:

1. Introduction of the problem (description of the case study)
2. Analysis (identification of the ethical issues raised by the case study)
3. Critical discussion
4. Conclusion

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

Documents and news

Academic year:

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 soon 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 Degree programme, students will need to submit and present their thesis/dissertation, which must be in English and focusing on a scientific topic covered during the programme. Alternatively, the thesis/dissertation may consist of the analysis and solution of a case study (theoretical and/or relevant to a real industrial context), experimental work, possibly developed as part of an internship, or original and independent research work that may include mathematical formalisation, computer design and a business-oriented approach.

These activities will be carried out under the guidance of a Thesis Supervisor at a University facility, or even outside the University of Verona, either in Italy or abroad, provided that they are recognised and accepted for this purpose in accordance with the teaching regulations of the Master's Degree programme in Data Science.

22 CFU credits shall be awarded for the final examination (assessment of the thesis/dissertation).

The Graduation Committee, which is in charge of the evaluation of the final examination (presentation of the dissertation in English) shall evaluate each candidate, based on their achievements throughout the entire degree programme, carefully assessing the degree of consistency between educational and professional objectives, as well as their ability for independent intellectual elaboration, critical thinking, communication skills and general cultural maturity, in relation to the objectives of the Master's Degree programme in Data Science, and in particular, in relation to the topics dealt with by the candidate in their thesis.

Students may take the final exam only after they have passed all the other modules and exams that are part of their individual study plan, and fulfil all the necessary administrative requirements, in accordance with the terms indicated in the General Study Manifesto.

The graduation exam and ceremony will be carried out by the Graduation Committee appointed by the Chair of the Teaching Committee and composed of a President and at least four other members chosen among the University's lecturers.

The thesis/dissertation will be assessed by the Dissertation Committee, which is composed of three lecturers possibly including the Thesis Supervisor, and appointed by the Chair of the Teaching Committee. The Dissertation Committee shall produce an evaluation of the dissertation, which will be submitted to the Graduation Committee, which will issue the final graduation mark. The Teaching Committee shall govern the procedures of the Dissertation Committee and the Graduation Committee, and any procedures relating to the score awarded for the final exam through specific regulations issued by the Teaching Committee.

Documents

Title Info File
File pdf Regolamento esame finale | Final exam regulation pdf, it, 387 KB, 27/04/22

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

Attendance

As stated in the Teaching Regulations for the A.Y. 2022/2023, attendance at the course of study is not mandatory.
 


Career management


Student login and resources


Erasmus+ and other experiences abroad