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 - II semestre Oct 2, 2017 Jun 15, 2018
I sem. Oct 2, 2017 Jan 31, 2018
II sem. Mar 1, 2018 Jun 15, 2018
Exam sessions
Session From To
Sessione invernale d'esami Feb 1, 2018 Feb 28, 2018
Sessione estiva d'esame Jun 18, 2018 Jul 31, 2018
Sessione autunnale d'esame Sep 3, 2018 Sep 28, 2018
Degree sessions
Session From To
Sessione di laurea estiva Jul 23, 2018 Jul 23, 2018
Sessione di laurea autunnale Oct 17, 2018 Oct 17, 2018
Sessione autunnale di laurea Nov 23, 2018 Nov 23, 2018
Sessione di laurea invernale Mar 22, 2019 Mar 22, 2019
Holidays
Period From To
Christmas break Dec 22, 2017 Jan 7, 2018
Easter break Mar 30, 2018 Apr 3, 2018
Patron Saint Day May 21, 2018 May 21, 2018
VACANZE ESTIVE Aug 6, 2018 Aug 19, 2018

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 Enrollment FAQs

Academic staff

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

Agostiniani Virginia

symbol email virginia.agostiniani@univr.it symbol phone-number +39 045 802 7979

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 0458027935

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

Canevari Giacomo

symbol email giacomo.canevari@univr.it symbol phone-number +390458027979

Chignola Roberto

symbol email roberto.chignola@univr.it symbol phone-number 045 802 7953

Daffara Claudia

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

Dai Pra Paolo

symbol email paolo.daipra@univr.it symbol phone-number +39 0458027093

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

Fioroni Tamara

symbol email tamara.fioroni@univr.it

Gnoatto Alessandro

symbol email alessandro.gnoatto@univr.it symbol phone-number 045 802 8537

Gonzato Guido

symbol email guido.gonzato@univr.it symbol phone-number 045 802 8303

Gregorio Enrico

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

Liptak Zsuzsanna

symbol email zsuzsanna.liptak@univr.it symbol phone-number +39 045 802 7032
foto,  June 25, 2020

Magazzini Laura

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

Mantese Francesca

symbol email francesca.mantese@univr.it symbol phone-number +39 0458027978

Mariotto Gino

symbol email gino.mariotto@univr.it
Foto,  October 5, 2015

Mazzuoccolo Giuseppe

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

Migliorini Sara

symbol email sara.migliorini@univr.it symbol phone-number +39 045 802 7908

Monti Francesca

symbol email francesca.monti@univr.it symbol phone-number 045 802 7910

Orlandi Giandomenico

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

Piccinelli Fabio

symbol email fabio.piccinelli@univr.it symbol phone-number +39 045 802 7097

Rizzi Romeo

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

Sansonetto Nicola

symbol email nicola.sansonetto@univr.it symbol phone-number 045-8027976

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 enrollment year.

CURRICULUM TIPO:

2° Year   activated in the A.Y. 2018/2019

ModulesCreditsTAFSSD
6
A
MAT/02
6
B
MAT/03
6
C
SECS-P/01
6
C
SECS-P/01
6
B
MAT/06

3° Year   activated in the A.Y. 2019/2020

ModulesCreditsTAFSSD
6
C
SECS-P/05
Final exam
6
E
-
activated in the A.Y. 2018/2019
ModulesCreditsTAFSSD
6
A
MAT/02
6
B
MAT/03
6
C
SECS-P/01
6
C
SECS-P/01
6
B
MAT/06
activated in the A.Y. 2019/2020
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 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

Coordinator

Paolo Dai Pra

Credits

6

Language

Italian

Scientific Disciplinary Sector (SSD)

MAT/06 - PROBABILITY AND STATISTICS

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,

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.

Students with disabilities or specific learning disorders (SLD), who intend to request the adaptation of the exam, must follow the instructions given HERE

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: only in this way will you be able to receive notification of all the notices from your teachers and your secretariat via email and soon also via the Univr app.

Graduation

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

Documents

Title Info File
File pdf 1. Come scrivere una tesi pdf, it, 31 KB, 29/07/21
File pdf 2. How to write a thesis pdf, it, 31 KB, 29/07/21
File pdf 5. Regolamento tesi pdf, it, 171 KB, 20/03/24

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
THESIS_1: Sensors and Actuators for Applications in Micro-Robotics and Robotic Surgery Various topics
THESIS_2: Force Feedback and Haptics in the Da Vinci Robot: study, analysis, and future perspectives Various topics
THESIS_3: Cable-Driven Systems in the Da Vinci Robotic Tools: study, analysis and optimization Various topics
Stage Research area
Internship proposals for students in mathematics Various topics

Attendance

As stated in the Teaching Regulations for the A.Y. 2022/2023, 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.
 


Career management


Student login and resources


Erasmus+ and other experiences abroad


Commissione tutor

La commissione ha il compito di guidare le studentesse e gli studenti durante l'intero percorso di studi, di orientarli nella scelta dei percorsi formativi, di renderli attivamente partecipi del processo formativo e di contribuire al superamento di eventuali difficoltà individuali.

E' composta dai proff. Sisto Baldo, Marco Caliari, Francesca Mantese, Giandomenico Orlandi e Nicola Sansonetto