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)




The teaching is organized as follows:

Parte I




I semestre

Academic staff

Paolo Dai Pra

Parte II




I semestre





I semestre

Academic staff

Luca Di Persio

Learning outcomes

The course will provide a self-contained and mathematically rigorous introduction to modern techniques of data analysis and modeling of random phenomena, with special emphasis to the theoretical bases, typical of probability theory, necessary to develop effective solutions to the challenges characterizing heterogeneous areas, eg , finance, fault-detection, innovation forecasting, energy prediction, etc., typical of Industry 4.0, with particular reference to the challenges posed in the field of big data analytics. The presentation of concepts, problems and related theoretical / practical solutions will be oriented to the applications, also making use of specific statistical software (e.g. Matlab, R, KNIME, etc.) always maintaining a high level of mathematical rigor. The course will discuss the basics of modern Probability theory (eg: random variables, their distributions and main statistical properties, convergence theorems and applications), with particular attention to the fundamental stochastic processes (eg: Markov chains , birth and death processes, code theory with real world applications) and their applications within real world scenarios characterized by the presence of big data and related time series.

At the end of the course the student has to show to have acquired the following skills:
● knowledge of the formal basis of probability theory
● ability to use the concepts of random variables (both in a discrete and continuous environment)
● ability to develop models based on known probabilistic models, e.g., v.a. binomial, Poisson, Gaussian, Gaussian mixtures, etc.
● understanding and knowing how to use the basic theory of stochastic processes, with particular reference to Markov chain theory (discrete and continuous time), birth and death processes and related applications
● know and know how to use the basic notions in descriptive and inferential statistics


Probability, conditioning and independence.

Random variables and their distributions. Discrete distributions. Expectation and variance. Continuous distributions.

Random vectors. Independence of random variables. Covariance and correlation.

Limit Theorems: Law of Large Numbers and Central Limit Theorem. Normal approximation.

Normal random vectors.

Discrete time Markov Chains. Markov Chain Monte Carlo.

Poisson Processes and Queuing Theory. Continuous time Markov Chains.

Introduction to random networks.

Examination Methods

The exam takes place in two parts.
The first part, mandatory for all students, consists of a written test with exercises.
The second part can be carried out, at the student's choice, in one of the following ways:
- oral exam, in which the student must be able to present the concepts and models described in the course, both in the theoretical and in the applicative aspects;
- a project assigned by the teacher, which will include the writing of a code for a simulation.


Reference texts
Activity Author Title Publishing house Year ISBN Notes
Parte II S. Ross A First Course in Probability (Edizione 10) Pearson 2018
Parte II P. Baldi Calcolo delle Probabilità McGraw Hill 2007 9788838663659
Parte II S. Ross Introduction to Probability models (Edizione 12) Academic Press 2019
Teoria Durret, R. Random graph dynamics Cambridge university press 2007
Teoria Bolloas, B. Random graphs Cambridge university press 2001
Teoria Chung, F. R. K. and Lu, L. Random graphs AMS Bookstore 2006
Teoria Duflo, M. Random Iterative Models, Applications of Mathematics, 34 SpringerVerlag, Berlin 1997
Teoria Albert, R. and Barab´asi, A.-L. Statistical mechanics of complex networks. Reviews of modern physics, 74(1):47. 2002 Reviews of modern physics, 74(1):47.

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.