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
Degree sessions
Session From To
Sessione di laurea estiva Jul 22, 2021 Jul 22, 2021
Sessione di laurea autunnale Oct 14, 2021 Oct 14, 2021
Sessione di laurea autunnale - Dicembre Dec 9, 2021 Dec 9, 2021
Sessione invernale di laurea Mar 16, 2022 Mar 16, 2022
Holidays
Period From To
Festa dell'Immacolata Dec 8, 2020 Dec 8, 2020
Vacanze Natalizie Dec 24, 2020 Jan 3, 2021
Vacanze di Pasqua Apr 2, 2021 Apr 6, 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

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

Albi Giacomo

giacomo.albi@univr.it +39 045 802 7913

Baldo Sisto

sisto.baldo@univr.it 045 802 7935

Bos Leonard Peter

leonardpeter.bos@univr.it +39 045 802 7987

Caliari Marco

marco.caliari@univr.it +39 045 802 7904

Canevari Giacomo

giacomo.canevari@univr.it +39 045 8027979

Chignola Roberto

roberto.chignola@univr.it 045 802 7953

Cubico Serena

serena.cubico@univr.it 045 802 8132

Daffara Claudia

claudia.daffara@univr.it +39 045 802 7942

Dai Pra Paolo

paolo.daipra@univr.it +39 0458027093

Daldosso Nicola

nicola.daldosso@univr.it +39 045 8027076 - 7828 (laboratorio)

Delledonne Massimo

massimo.delledonne@univr.it 045 802 7962; Lab: 045 802 7058

De Sinopoli Francesco

francesco.desinopoli@univr.it 045 842 5450

Di Persio Luca

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

Favretto Giuseppe

giuseppe.favretto@univr.it +39 045 802 8749 - 8748

Fioroni Tamara

tamara.fioroni@univr.it 0458028489

Gnoatto Alessandro

alessandro.gnoatto@univr.it 045 802 8537

Gregorio Enrico

Enrico.Gregorio@univr.it 045 802 7937

Mantese Francesca

francesca.mantese@univr.it +39 045 802 7978

Marigonda Antonio

antonio.marigonda@univr.it +39 045 802 7809

Mattiolo Davide

davide.mattiolo@univr.it

Mazzuoccolo Giuseppe

giuseppe.mazzuoccolo@univr.it +39 0458027838

Monti Francesca

francesca.monti@univr.it 045 802 7910

Nardon Chiara

chiara.nardon@univr.it

Orlandi Giandomenico

giandomenico.orlandi at univr.it 045 802 7986

Patacca Marco

marco.patacca@univr.it 0458028788

Rizzi Romeo

romeo.rizzi@univr.it +39 045 8027088

Sala Pietro

pietro.sala@univr.it 0458027850

Sansonetto Nicola

nicola.sansonetto@univr.it 049-8027932

Schuster Peter Michael

peter.schuster@univr.it +39 045 802 7029

Segala Roberto

roberto.segala@univr.it 045 802 7997

Solitro Ugo

ugo.solitro@univr.it +39 045 802 7977

Vincenzi Elia

elia.vincenzi@univr.it

Zuccher Simone

simone.zuccher@univr.it

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.

CURRICULUM TIPO:
ModulesCreditsTAFSSD
6
A
(MAT/02)
6
B
(MAT/03)
6
C
(SECS-P/01)
6
C
(SECS-P/01)
English language B1 level
6
E
-
ModulesCreditsTAFSSD
6
C
(SECS-P/05)
Final exam
6
E
-

2° Year

ModulesCreditsTAFSSD
6
A
(MAT/02)
6
B
(MAT/03)
6
C
(SECS-P/01)
6
C
(SECS-P/01)
English language B1 level
6
E
-

3° Year

ModulesCreditsTAFSSD
6
C
(SECS-P/05)
Final exam
6
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°- 3°
Between the years: 1°- 2°- 3°
Other activities
6
F
-

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

4S00254

Coordinatore

Paolo Dai Pra

Credits

6

Scientific Disciplinary Sector (SSD)

MAT/06 - PROBABILITY AND STATISTICS

Language

Italian

Period

I semestre dal Oct 1, 2020 al Jan 29, 2021.

Learning outcomes

The Stochastic Systems course aims at giving an introduction to the basic concepts underlying the rigorous mathematical description of the temporal dynamics for random quantities. The course prerequisites are those of a standard course in Probability, for Mathematics / Physics. It is supposed that students are familiar with the basics Probability calculus, in the Kolmogorov assiomatisation setting, in particular with respect to the concepts of density function, probability distribution, conditional probability, conditional expectation for random variables, measure theory (basic ), characteristic functions of random variables, convrgence theorems (in measure, almost everywhere, etc.), central limit theorem and its (basic) applications, etc. The Stochastic Systems course aims, in particular, to provide the basic concepts of: Filtered probability space, martingale processes, stopping times, Doob theorems, theory of Markov chains in discrete and continuous time (classification of states, invariant and limit,measures, ergodic theorems, etc.), basics on queues theory and an introduction to Brownian motion. A part of the course is devoted to the computer implementation of operational concepts underlying the discussion of stochastic systems of the Markov chain type, both in discrete and continuous time. A part of the course is dedicated to the introduction and the operational study, via computer simulations, to univariate time series. It is important to emphasize how the Stochastic Systems course is organized in such a way that students can concretely complete and further develop their own: capacity of analysis, synthesis and abstraction; specific computational and computer skills; ability to understand texts, even advanced, of Mathematics in general and Applied Mathematics in particular; ability to develop mathematical models for physical and natural sciences, while being able to analyze its limits and actual applicability, even from a computational point of view; skills concerning how to develop mathematical and statistical models for the economy and financial markets; capacity to extract qualitative information from quantitative data; knowledge of programming languages or specific software.

Program

The entire course will be available online. In addition, a number of the lessons/all the lessons (see the course
schedule) will be held in-class.

1. Discrete-time Markov chains. Markov properties and transition probability. Irreducibility, aperiodicity. Stationary distributions. Reversible distributions.

2. Hitting times. Convergence to the stationary distribution. Law of large numbers for Markov chains. MCMC: Metropolis algorithm and Gibbs sampler.

3. Reducible Markov chains. Transient and recurring states. Probability of absorption.

4 .. Markov chains with countable states. Recurrence and transience of the random walk on Z ^ d. Positive recurrent states and stationary distributions. Convergence theorem for irreducible Markov chains with countable states.

5. Continuous-time Markov chains. The Poisson process and its properties. Continuous-time Markov property. Semigroup associated with a Markov chain: continuity and differentiability; generator. Kolmogorov equations. Stationary distributions. Dynkin's formula. Probabilistic construction of a continuous-time Markov chain.

6. Erdos-Renyi random graphs. Model definition. Connected components.

7. Conditional Expectation and Conditional Distribution. Martingale. Stopping theorem and convergence theorem.

Bibliografia

Reference texts
Author Title Publishing house Year ISBN Notes
Levin, David A., and Yuval Peres Markov chains and mixing times American Mathematical Society 2017

Examination Methods

Written exam, with exercises and theoretical questions.

The assessment methods could change according to the academic rules

Type D and Type F activities

Le attività formative in ambito D o F comprendono gli insegnamenti impartiti presso l'Università di Verona o periodi di stage/tirocinio professionale.
Nella scelta delle attività di tipo D, gli studenti dovranno tener presente che in sede di approvazione si terrà conto della coerenza delle loro scelte con il progetto formativo del loro piano di studio e dell'adeguatezza delle motivazioni eventualmente fornite.

 

I semestre From 10/1/20 To 1/29/21
years Modules TAF Teacher
1° 2° History and Didactics of Geology D Guido Gonzato (Coordinatore)
1° 2° 3° Algorithms D Roberto Segala (Coordinatore)
1° 2° 3° Scientific knowledge and active learning strategies F Francesca Monti (Coordinatore)
1° 2° 3° Genetics D Massimo Delledonne (Coordinatore)
II semestre From 3/1/21 To 6/11/21
years Modules TAF Teacher
1° 2° 3° Algorithms D Roberto Segala (Coordinatore)
1° 2° 3° Python programming language D Vittoria Cozza (Coordinatore)
1° 2° 3° Organization Studies D Giuseppe Favretto (Coordinatore)
List of courses with unassigned period
years Modules TAF Teacher
Subject requirements: mathematics D Rossana Capuani
1° 2° 3° ECMI modelling week F Not yet assigned
1° 2° 3° ESA Summer of code in space (SOCIS) F Not yet assigned
1° 2° 3° Google summer of code (GSOC) F Not yet assigned
1° 2° 3° Introduzione all'analisi non standard F Sisto Baldo
1° 2° 3° C Programming Language D Pietro Sala (Coordinatore)
1° 2° 3° LaTeX Language D Enrico Gregorio (Coordinatore)

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


Graduation

Attachments

List of theses and work experience proposals

theses proposals Research area
Formule di rappresentazione per gradienti generalizzati Mathematics - Analysis
Formule di rappresentazione per gradienti generalizzati Mathematics - Mathematics
Mathematics Bachelor and Master thesis titles Various topics
Stage Research area
Internship proposals for students in mathematics Various topics

Attendance

As stated in point 25 of the Teaching Regulations for the A.Y. 2021/2022, except for specific practical or lab activities, attendance is not mandatory. Regarding these activities, please see the web page of each module for information on the number of hours that must be attended on-site.
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.