Studying at the University of Verona

A.A. 2020/2021

Academic calendar

Il calendario accademico riporta le scadenze, gli adempimenti e i periodi rilevanti per la componente studentesca, personale docente e personale dell'Università. Sono inoltre indicate le festività e le chiusure ufficiali dell'Ateneo.
L’anno accademico inizia il 1° ottobre e termina il 30 settembre dell'anno successivo.

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

The exam roll calls are centrally administered by the operational unit  Science and Engineering Teaching and Student Services Unit
Exam Session Calendar and Roll call enrolment sistema ESSE3. If you forget your password to the online services, please contact the technical office in your Faculty or to the service credential recovery.

Exam calendar

Per dubbi o domande Read the answers to the more serious and frequent questions - F.A.Q. Examination enrolment

Academic staff

B C D F G H I P Q S Z

Badino Massimiliano

massimiliano.badino@univr.it +39 045 802 8459

Bazzani Claudia

claudia.bazzani@univr.it 0458028734

Bullini Orlandi Ludovico

ludovico.bulliniorlandi@univr.it 045 802 8095

Carra Damiano

damiano.carra@univr.it +39 045 802 7059

Carradore Marco

marco.carradore@univr.it

Castellini Alberto

alberto.castellini@univr.it +39 045 802 7908

Ceccato Mariano

mariano.ceccato@univr.it

Chiarini Andrea

andrea.chiarini@univr.it 045 802 8223

Cordoni Francesco Giuseppe

francescogiuseppe.cordoni@univr.it

Dai Pra Paolo

paolo.daipra@univr.it +39 0458027093

Dalla Preda Mila

mila.dallapreda@univr.it

Di Persio Luca

luca.dipersio@univr.it +39 045 802 7968

Farinelli Alessandro

alessandro.farinelli@univr.it +39 045 802 7842

Giachetti Andrea

andrea.giachetti@univr.it +39 045 8027998

Paci Federica Maria Francesca

federicamariafrancesca.paci@univr.it +39 045 802 7909

Quintarelli Elisa

elisa.quintarelli@univr.it +39 045 802 7852

Spoto Nicola Fausto

fausto.spoto@univr.it +39 045 8027940

Zardini Alessandro

alessandro.zardini@univr.it 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.

TeachingsCreditsTAFSSD
TeachingsCreditsTAFSSD
9
B/C
(IUS/01 ,M-FIL/03)
Training
6
F
-
Final exam
22
E
-

1° Anno

TeachingsCreditsTAFSSD

2° Anno

TeachingsCreditsTAFSSD
9
B/C
(IUS/01 ,M-FIL/03)
Training
6
F
-
Final exam
22
E
-
Teachings Credits TAF SSD
Between the years: 1°- 2°1 module to be chosen among the following
6
C
(SPS/07)
6
C
(IUS/17)
Between the years: 1°- 2°2 courses to be chosen among the following
6
B
(SECS-P/10)
Between the years: 1°- 2°2 courses to be chosen among the following
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

4S009077

Credits

12

Scientific Disciplinary Sector (SSD)

MAT/06 - PROBABILITY AND STATISTICS

Language of instruction

English

The teaching is organized as follows:

Parte I

Credits

4

Period

I semestre

Academic staff

Paolo Dai Pra

Parte II

Credits

7

Period

I semestre

Academic staff

Francesco Giuseppe Cordoni

Teoria

Credits

1

Period

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

Program

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.

Bibliografia

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.

Tipologia di Attività formativa D e F

Academic year

Career prospects


Avvisi degli insegnamenti e del corso di studio

Per la comunità studentesca

Se sei già iscritta/o a un corso di studio, puoi consultare tutti gli avvisi relativi al tuo corso di studi nella tua area riservata MyUnivr.
In questo portale potrai visualizzare informazioni, risorse e servizi utili che riguardano la tua carriera universitaria (libretto online, gestione della carriera Esse3, corsi e-learning, email istituzionale, modulistica di segreteria, procedure amministrative, ecc.).
Entra in MyUnivr con le tue credenziali GIA.

University Language Centre - CLA


Tirocini e stage

Le attività di stage sono finalizzate a far acquisire allo studente una conoscenza diretta in settori di particolare attività per l’inserimento nel mondo del lavoro e per l’acquisizione di abilità specifiche di interesse professionale.
Le attività di stage sono svolte sotto la diretta responsabilità di un singolo docente presso studi professionali, enti della pubblica amministrazione, aziende accreditate dall’Ateneo veronese.
I crediti maturati in seguito ad attività di stage saranno attribuiti secondo quanto disposto nel dettaglio dal “Regolamento d’Ateneo per il riconoscimento dei crediti maturati negli stage universitari” vigente.

Tutte le informazioni in merito agli stage sono reperibili al link https://www.univr.it/it/i-nostri-servizi/stage-e-tirocini.
 

Graduation

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

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.