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 Enrolment FAQs

Academic staff

B C D F G H I P Q S Z

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
LBO,  January 31, 2017

Bullini Orlandi Ludovico

symbol email ludovico.bulliniorlandi@univr.it symbol phone-number 045 802 8095

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

Cordoni Francesco Giuseppe

symbol email francescogiuseppe.cordoni@univr.it

Dai Pra Paolo

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

Dalla Preda Mila

symbol email mila.dallapreda@univr.it

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

Giachetti Andrea

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

Paci Federica Maria Francesca

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

Quintarelli Elisa

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

Spoto Nicola Fausto

symbol email fausto.spoto@univr.it symbol phone-number +39 045 8027940

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 enrolment year.

activated in the A.Y. 2021/2022
ModulesCreditsTAFSSD
Training
6
F
-
Final exam
22
E
-

1° Year

ModulesCreditsTAFSSD

2° Year 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

4S009088

Credits

6

Language

English en

Scientific Disciplinary Sector (SSD)

M-FIL/02 - LOGIC AND PHILOSOPHY OF SCIENCE

Period

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.

Program

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%)

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Type D and Type F activities

Documents and news

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.

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.

Attachments

Title Info File
Doc_Univr_pdf Regolamento esame finale | Final exam regulation 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.
Please refer to the Crisis Unit's latest updates for the mode of teaching.


Career management


Student login and resources