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
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 Enrolment FAQs

Academic staff


Badino Massimiliano +39 045 802 8459

Bazzani Claudia 0458028734
LBO,  January 31, 2017

Bullini Orlandi Ludovico 045 802 8095

Carra Damiano +39 045 802 7059

Carradore Marco

Castellini Alberto +39 045 802 7908

Ceccato Mariano

Chiarini Andrea 045 802 8223

Cordoni Francesco Giuseppe

Dai Pra Paolo +39 0458027093

Dalla Preda Mila

Di Persio Luca +39 045 802 7968

Farinelli Alessandro +39 045 802 7842

Giachetti Andrea +39 045 8027998

Paci Federica Maria Francesca +39 045 802 7909

Quintarelli Elisa +39 045 802 7852

Spoto Nicola Fausto +39 045 8027940

Zardini Alessandro 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 enrolment year.

(IUS/01 ,M-FIL/03)
Final exam

1° Year


2° Year

(IUS/01 ,M-FIL/03)
Final exam
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)
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.

SPlacements in companies, public or private institutions and professional associations

Teaching code




Scientific Disciplinary Sector (SSD)



English en


II semestre dal Mar 1, 2021 al Jun 11, 2021.

Learning outcomes

The course will allow the student to acquire the skills necessary to apply the key concepts of epistemology (knowledge, methodology, justification, explanation, etc.) to the specific case of data science and to the discussion of consequences and implications of big data for society in general.

At the end of the course the student has to show to have acquired the following skills:
● recognize and discuss the main epistemological issues relating to the knowledge produced by the collection and manipulation of big data, in particular for what concerns the topics: (1) epistemological specificity of big data; (2) the impact of big data on scientific work; (3) Big Data and cultural authority of science
● having acquired, through detailed analysis of real life situations, the tools for a more conscious and critical approach to the work of data analyst, as well as for the management and dissemination of big data in public domains.


The course is dedicated to exploring the epistemological, social, and political issues related to big data, machine learning, and artificial intelligence. The program is divided into two main moduli:

(A) Producing knowledge in the digital age. This module will deal with the epistemological questions raised by the use of machine learning and big data in the production of scientific knowledge. Examples of such questions are: How do big data change scientific practices and methods? What are the limits of the computational approach to science? Do big data make theories superfluous? What are the epistemological features of statistical learning? The structure of the module is as follows:

(A.1) Introduction to the epistemology of computability: complexity and undecidability.
(A.2) The concept of data and the computational theory of the mind.
(A.3) Machine Learning, big data, and the scientific method.

(B) The social epistemology of big data. The second module is concerned with the socio-epistemological and political impact of machine learning and big data on scientific practice and society at large. Examples of questions tackled in this module are: How does the social structure of scientific research change as a result of using big data? How can one make artificial intelligence more explainable and accountable? How does machine learning affect digital environments such as social networks? The structure of the module is as follows:

(B.1) Scientific research and Big Data.
(B.2) Explainable Artificial Intelligence
(B.3.) Truth and post-truth in digital environments.


Reference texts
Author Title Publishing house Year ISBN Notes
Donald Gillies Artificial Intelligence and Scientific Method Oxford University Press 1996
Marcello Frixione e Dario Palladino Funzioni, macchine, algoritmi Carocci 2004
Sabina Leonelli La ricerca scientifica nell'era dei big data Meltemi 2018
Nils Nilsson The Quest for Artificial Intelligence: A History of Ideas and Achievements Cambridge University Press 2009
Francesco Berto Tutti pazzi per Gödel Laterza 2008

Examination Methods

The course will combine introductory lectures and class discussions in the form of reading seminars. The final assessment is the result of three elements:

(1) A class presentation of a text or an issue (30%)
(2) A written assignment (max 3000 words) (30%)
(3) Oral exam (40%)

Type D and Type F activities

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.

Gestione carriere


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


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.

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.