Studying at the University of Verona

A.A. 2019/2020

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, 2019 Jan 31, 2020
II semestre Mar 2, 2020 Jun 12, 2020
Exam sessions
Session From To
Sessione invernale d'esame Feb 3, 2020 Feb 28, 2020
Sessione estiva d'esame Jun 15, 2020 Jul 31, 2020
Sessione autunnale d'esame Sep 1, 2020 Sep 30, 2020
Degree sessions
Session From To
Sessione estiva di laurea Jul 22, 2020 Jul 22, 2020
Sessione autunnale di laurea Oct 14, 2020 Oct 14, 2020
Sessione autunnale di laurea solo triennale Dec 10, 2020 Dec 10, 2020
Sessione invernale di laurea Mar 16, 2021 Mar 16, 2021
Holidays
Period From To
Festa di Ognissanti Nov 1, 2019 Nov 1, 2019
Festa dell'Immacolata Dec 8, 2019 Dec 8, 2019
Vacanze di Natale Dec 23, 2019 Jan 6, 2020
Vacanze di Pasqua Apr 10, 2020 Apr 14, 2020
Festa della Liberazione Apr 25, 2020 Apr 25, 2020
Festa del lavoro May 1, 2020 May 1, 2020
Festa del Santo Patrono May 21, 2020 May 21, 2020
Festa della Repubblica Jun 2, 2020 Jun 2, 2020
Vacanze estive Aug 10, 2020 Aug 23, 2020

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

A B C D F G L M O P R S Z

Albi Giacomo

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

Angeleri Lidia

lidia.angeleri@univr.it 045 802 7911

Baldo Sisto

sisto.baldo@univr.it 045 802 7935

Bos Leonard Peter

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

Boscaini Maurizio

maurizio.boscaini@univr.it

Busato Federico

federico.busato@univr.it

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

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)

De Sinopoli Francesco

francesco.desinopoli@univr.it 045 842 5450

Di Persio Luca

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

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

Liptak Zsuzsanna

zsuzsanna.liptak@univr.it +39 045 802 7032

Magazzini Laura

laura.magazzini@univr.it 045 8028525

Mantese Francesca

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

Mariotto Gino

gino.mariotto@univr.it +39 045 8027031

Mazzuoccolo Giuseppe

giuseppe.mazzuoccolo@univr.it +39 0458027838

Migliorini Sara

sara.migliorini@univr.it +39 045 802 7908

Monti Francesca

francesca.monti@univr.it 045 802 7910

Orlandi Giandomenico

giandomenico.orlandi at univr.it 045 802 7986

Piccinelli Fabio

fabio.piccinelli@univr.it +39 045 802 7097

Rizzi Romeo

romeo.rizzi@univr.it +39 045 8027088

Sansonetto Nicola

nicola.sansonetto@univr.it 049-8027932

Schuster Peter Michael

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

Solitro Ugo

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

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:
TeachingsCreditsTAFSSD
6
B
(MAT/03)
6
A
(MAT/02)
6
C
(SECS-P/01)
6
C
(SECS-P/01)
English B1
6
E
-
TeachingsCreditsTAFSSD
6
C
(SECS-P/05)
9
C
(SECS-S/06)
Final exam
6
E
-

2° Anno

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

3° Anno

TeachingsCreditsTAFSSD
6
C
(SECS-P/05)
9
C
(SECS-S/06)
Final exam
6
E
-
Teachings 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.




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 of instruction

Italian

Period

I semestre dal Oct 1, 2019 al Jan 31, 2020.

Learning outcomes

Stochastic Systems [ Applied Mathematics ]
AA 2018/2019

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 2018/2019 Syllabus

1) Markov chains with discrete time and finite state space: irreducibility and aperiodicity, stationary distributions, classification of states, MCMC.

2) Markov chains with countable state space: recurrence, positivity.

3) The Poisson process and other counting processes. Introduction to queuing theory.

4) Markov chains with finite state space and continuous time: associated semigroup, generator, stationary distributions, Kolmogorov equations, rate of convergence to equilibrium and functional inequalities,

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 Scaricabile alla pagina https://s3.amazonaws.com/academia.edu.documents/30694248/recent.pdf?response-content-disposition=inline%3B%20filename%3DMarkov_chains_and_mixing_times.pdf&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWOWYYGZ2Y53UL3A%2F20191005%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20191005T133241Z&X-Amz-Expires=3600&X-Amz-SignedHeaders=host&X-Amz-Signature=3c046ef319a0d4eaa4a83f4138d7950cb982f2f0c351b6f2e135234f11790559

Examination Methods

Stochastic Systems [ Applied Mathematics ]
AA 2018/2019

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.

Tipologia di Attività formativa D e F

Academic year
I semestre From 10/1/19 To 1/31/20
years Teachings TAF Teacher
1° 2° 3° Python programming language D Maurizio Boscaini (Coordinatore)
1° 2° 3° SageMath F Zsuzsanna Liptak (Coordinatore)
1° 2° 3° History of Modern Physics 2 D Francesca Monti (Coordinatore)
1° 2° 3° History and Didactics of Geology D Guido Gonzato (Coordinatore)
II semestre From 3/2/20 To 6/12/20
years Teachings TAF Teacher
1° 2° 3° C Programming Language D Sara Migliorini (Coordinatore)
1° 2° 3° C++ Programming Language D Federico Busato (Coordinatore)
1° 2° 3° LaTeX Language D Enrico Gregorio (Coordinatore)
List of courses with unassigned period
years Teachings TAF Teacher
1° 2° 3° Corso Europrogettazione D Not yet assigned
1° 2° 3° Corso online ARPM bootcamp F Not yet assigned
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

Career prospects


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Graduation

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

University Language Centre - CLA


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.