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, 2015 Jan 29, 2016
II semestre Mar 1, 2016 Jun 10, 2016
Exam sessions
Session From To
Sessione straordinaria Appelli d'esame Feb 1, 2016 Feb 29, 2016
Sessione estiva Appelli d'esame Jun 13, 2016 Jul 29, 2016
Sessione autunnale Appelli d'esame Sep 1, 2016 Sep 30, 2016
Degree sessions
Session From To
Sess. autun. App. di Laurea Oct 12, 2015 Oct 12, 2015
Sess. autun. App. di Laurea Nov 26, 2015 Nov 26, 2015
Sess. invern. App. di Laurea Mar 15, 2016 Mar 15, 2016
Sess. estiva App. di Laurea Jul 19, 2016 Jul 19, 2016
Sess. autun. 2016 App. di Laurea Oct 11, 2016 Oct 11, 2016
Sess. autun 2016 App. di Laurea Nov 30, 2016 Nov 30, 2016
Sess. invern. 2017 App. di Laurea Mar 16, 2017 Mar 16, 2017
Holidays
Period From To
Festività dell'Immacolata Concezione Dec 8, 2015 Dec 8, 2015
Vacanze di Natale Dec 23, 2015 Jan 6, 2016
Vacanze Pasquali Mar 24, 2016 Mar 29, 2016
Anniversario della Liberazione Apr 25, 2016 Apr 25, 2016
Festa del S. Patrono S. Zeno May 21, 2016 May 21, 2016
Festa della Repubblica Jun 2, 2016 Jun 2, 2016
Vacanze estive Aug 8, 2016 Aug 15, 2016

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 G M O R S Z

Albi Giacomo

symbol email giacomo.albi@univr.it symbol phone-number +39 045 802 7913

Angeleri Lidia

symbol email lidia.angeleri@univr.it symbol phone-number 045 802 7911

Baldo Sisto

symbol email sisto.baldo@univr.it symbol phone-number 045 802 7935

Bos Leonard Peter

symbol email leonardpeter.bos@univr.it symbol phone-number +39 045 802 7987

Boscaini Maurizio

symbol email maurizio.boscaini@univr.it

Busato Federico

symbol email federico.busato@univr.it

Caliari Marco

symbol email marco.caliari@univr.it symbol phone-number +39 045 802 7904

Cordoni Francesco Giuseppe

symbol email francescogiuseppe.cordoni@univr.it

Daffara Claudia

symbol email claudia.daffara@univr.it symbol phone-number +39 045 802 7942

Daldosso Nicola

symbol email nicola.daldosso@univr.it symbol phone-number +39 045 8027076 - 7828 (laboratorio)

De Sinopoli Francesco

symbol email francesco.desinopoli@univr.it symbol phone-number 045 842 5450

Di Persio Luca

symbol email luca.dipersio@univr.it symbol phone-number +39 045 802 7968

Gregorio Enrico

symbol email Enrico.Gregorio@univr.it symbol phone-number 045 802 7937

Magazzini Laura

symbol email laura.magazzini@univr.it symbol phone-number 045 8028525

Malachini Luigi

symbol email luigi.malachini@univr.it symbol phone-number 045 8054933

Mantese Francesca

symbol email francesca.mantese@univr.it symbol phone-number +39 045 802 7978

Marigonda Antonio

symbol email antonio.marigonda@univr.it symbol phone-number +39 045 802 7809

Mariotto Gino

symbol email gino.mariotto@univr.it symbol phone-number +39 045 8027031

Mariutti Gianpaolo

symbol email gianpaolo.mariutti@univr.it symbol phone-number 045 802 8241

Mazzuoccolo Giuseppe

symbol email giuseppe.mazzuoccolo@univr.it symbol phone-number +39 0458027838

Orlandi Giandomenico

symbol email giandomenico.orlandi at univr.it symbol phone-number 045 802 7986

Rizzi Romeo

symbol email romeo.rizzi@univr.it symbol phone-number +39 045 8027088

Rossi Francesco

Schuster Peter Michael

symbol email peter.schuster@univr.it symbol phone-number +39 045 802 7029

Solitro Ugo

symbol email ugo.solitro@univr.it symbol phone-number +39 045 802 7977

Zuccher Simone

symbol email 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.

ModulesCreditsTAFSSD
6
A
MAT/02
One course to be chosen among the following
6
C
SECS-P/01
6
C
FIS/01
6
B
MAT/03
One course to be chosen among the following
6
C
SECS-P/01
6
B
MAT/06
ModulesCreditsTAFSSD
One/two courses to be chosen among the following
6
C
SECS-P/05
Prova finale
6
E
-

2° Year

ModulesCreditsTAFSSD
6
A
MAT/02
One course to be chosen among the following
6
C
SECS-P/01
6
C
FIS/01
6
B
MAT/03
One course to be chosen among the following
6
C
SECS-P/01
6
B
MAT/06

3° Year

ModulesCreditsTAFSSD
One/two courses to be chosen among the following
6
C
SECS-P/05
Prova finale
6
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°- 3°
Between the years: 1°- 2°- 3°
Other activitites
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.




S Placements in companies, public or private institutions and professional associations

Teaching code

4S00254

Coordinatore

Luca Di Persio

Credits

6

Language

Italian

Scientific Disciplinary Sector (SSD)

MAT/06 - PROBABILITY AND STATISTICS

Period

I sem. dal Oct 2, 2017 al Jan 31, 2018.

Learning outcomes

Stochastic Systems [ Applied Mathematics ]
AA 2017/20178

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

Stochastic Systems [ Applied Mathematics ] AA 2017/2018 Syllabus • Conditional Expectation ( from Chap.1 of [BMP] ) • Definitions and basic properties • Conditional expectations and conditional laws • Introduction to stochastic processes ( From Chap.1 di [BMP] ) • Filtered probability space, filtrations • Adapted stochastic process (wrt a given filtration) • Martingale (first definitions and examples: Markov chains) • Kolmogorov characterization theorem • Stopping times • Martingale ( From Chap.3 of [BMP] • Definition of martingale process, resp. super, resp. lower, martingale • Fundamental properties • Stopping times for martingale processes • Convergence theorems for martingales • Markov chains (MC) ( From Chap.4 of [Beichelet] , Chap.5 of di [Baldi] ) • Transition matrix for a MC • Construction and existence for MC • Omogeneous MC (with respect to time and space) • Canonical MC • Classification of states for a given MC ( and associated classes ) • Chapman-Kolmogorov equation • Recurrent, resp. transient, states ( classification criteria ) • Irriducible and recurrent chains • Invariant (stationary) measures, ergodic measures, limit measures ( Ergodic theorem ) • Birth and death processes (discrete time) • Continuous time MC ( From Chap.5 of [Beichelt] ) • Basic definitions • Chapman-Kolmogorov equations • Absolute and stationary distributions • States classifications • Probability and rates of transition • Kolmogorov differential equations • Stationary laws • Birth and death processes ( first steps in continuous time ) • Queuing theory (first steps in continuous time) • Point, Counting and Poisson Processes ( From Chap.3 of [Beichelt] ) • Basic definitions and properties • Stochastic point processes (SPP) and Stochastic Counting Processes (SCP) • Marked SPP • Stationarity, intensity and composition for SPP and SCP • Homogeneous Poisson Processes (HPP) • Non Homogeneous Poisson Processes (nHPP) • Mixed Poisson Processes (MPP) • Birth and Death processes (B&D) ( From Chap.5 of [Beichelt] ) • Birth processes • Death processes • B&D processes ° Time-dependent state probabilities ° Stationary state probabilities ° Inhomogeneous B&D processes Bibliography [Baldi] P. Baldi, Calcolo delle Probabilità, McGraw-Hill Edizioni (Ed. 01/2007) [Beichelt] F. Beichelt, Stochastic Processes in Science, Engineering and Finance, Chapman & Hall/CRC, Taylor & Francis group, (Ed. 2006) [BPM] P. Baldi, L. Matzliak and P. Priouret, Martingales and Markov Chains – Solve Exercises and Elements of Theory, Chapman & Hall/CRC (English edition, 2002) Further interesting books are: N. Pintacuda, Catene di Markov, Edizioni ETS (ed. 2000) Brémaud, P., Markov Chains. Gibbs Fields, Monte Carlo Simulation, and Queues, Texts in Applied Mathematics, 31. Springer-Verlag, New York, 1999 Duflo, M., Random Iterative Models, Applications of Mathematics, 34. SpringerVerlag, Berlin, 1997 Durrett, R., Probability: Theory and Examples, Wadsworth and Brooks, Pacific Grove CA, 1991 Grimmett, G. R. and Stirzaker, D. R., Probability and Random Processes. Solved Problems. Second edition. The Clarendon Press, Oxford University Press, New York, 1991 Hoel, P. G., Port, S. C. and Stone, C. J., Introduction to Stochastic Processes, Houghton Mifflin, Boston, 1972

Reference texts
Author Title Publishing house Year ISBN Notes
P. Baldi Calcolo delle Probabilità McGraw Hill 2007 9788838663659
Levin, David A., and Yuval Peres Markov chains and mixing times American Mathematical Society 2017
P. Baldi, L. Matzliak and P. Priouret Martingales and Markov Chains – Solve Exercises and Elements of Theory Chapman & Hall/CRC (English edition) 2002
G. R. Grimmett, D. R. Stirzaker Probability and Random Processes: Solved Problems (Edizione 2) The Clarendon Press, Oxford University Press, New York 1991

Examination Methods

Stochastic Systems [ Applied Mathematics ]
AA 2017/2018

The course is diveded into the following three parts

1) Theory of stochastic systems
2) Introduction to time-series analysis
3) Computer exercises ( mainly based on the theory of Markov Chains, in discrete as well in continuous time )

Part (2) will be mainly performed in laboratory mode, using computer equipped classrooms, with the possibility, for each student to use a computer in order to implement , real time, the models proposed during the lesson. This activity will be supported by a tutor for a total amount of 24 (frontal) hours.

Part (3) will be taught by Prof. Caliari in a computer equipped laboratory.

The exam will be subdivided into the following three parts

* a written exam concerning point (1)
* a project presented in agreement with the programme developed with prof. Marco Caliari (point 3)
* exercises and a project concerning point (2)

The programme concerning the written exam, with respect to point (1), is the one reported in the Program section.
The project to be presented with prof. Caliari has to be decided with him.
The project to be presented with respect to point (2), will be chosen, by each student, within the the following list

@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
@Projects
@
@Warning: Since the list of projects may vary during the year, Students are warmly invited to directly contact prof. Di @Persio in order to choose the right project to develop, within the list of arguments that will be actually developed @during laboratory hours
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@

1-Compare the following methods of estimate and/or elimination of time series trends

*First order differences study
*Smoothing with moving average filter
*Fourier transform
*Exponential Smoothing
*Polynomial Data fitting

2- Describe and provide a numerical implementation of the one-step predictor for the following models

FIR(4)
ARX(3,1)
OE(3,1)
ARMA(2,3)
ARMAX(2,1,2)
Box-Jenkins(nb,nc,nd,nf)

3- Compare the Prediction Error Minimization (PEM) and the Maximum Likelihood (ML) approach for the identification of the model parameters (it requires a personal effort in the homes ML)

4- Provide a concrete implementation for the k-fold cross-validation, e.g. using Matlab/Octave, following the example-test that has been given during the lessons

5-Detailed explanation of (at least) one of the following test
*Shapiro-Wilk
*Kolmogorov-Smirnov
*Lilliefors

Practical implementation of the project chosen by the student can be realized exploiting one of the following software frameworks : R, Python, Matlab, Gnu Octave, Excel

The final grade, expressed in thirtieths, will result from the following formula
Rating = (5/6) * T + (1/6) * E + P
where
T is the mark out of 30 on the part of Theory (written exam with prof. Di Persio)
It is the mark out of 30 on the part of Exercises (oral exam with prof. Caliari)
P is a score within the range [0,2]

It is important to emphasize how the objectives of the exam are also centered on assessing the individual student's ability to:

° carry out technical tasks defined in the model-mathematical settings;
° extract qualitative information from quantitative data with particular reference to the analysis of historical series, the study and the realization of predictive models, the development of automatic processes in the analysis of random phenomena;
° use computer/software tools such as R, Matlab, Gnu Octave, etc. , to realize models analyzed in the course and / or implemented in laboratory hours.

???AdattamentoProvaEsameDSA???

Type D and Type F activities

Modules not yet included

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

For schedules, administrative requirements and notices on graduation sessions, please refer to the Graduation Sessions - Science and Engineering service.

Attachments

Title Info File
Doc_Univr_pdf 1. Come scrivere una tesi 31 KB, 29/07/21 
Doc_Univr_pdf 2. How to write a thesis 31 KB, 29/07/21 
Doc_Univr_pdf 5. Regolamento tesi (valido da luglio 2022) 171 KB, 17/02/22 

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
Proposte Tesi A. Gnoatto Various topics
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.

Career management


Area riservata studenti