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.
Course calendar
The Academic Calendar sets out the degree programme lecture and exam timetables, as well as the relevant university closure dates..
Period | From | To |
---|---|---|
I semestre | Oct 1, 2019 | Jan 31, 2020 |
II semestre | Mar 2, 2020 | Jun 12, 2020 |
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 |
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 |
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
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.
Academic staff
Raffaele Alice
alice.raffaele@univr.itStudy 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.
1° Year
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2° Year activated in the A.Y. 2020/2021
Modules | Credits | TAF | SSD |
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3° Year activated in the A.Y. 2021/2022
Modules | Credits | TAF | SSD |
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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.
Stochastic systems (2021/2022)
Teaching code
4S00254
Academic staff
Coordinator
Credits
6
Language
Italian
Scientific Disciplinary Sector (SSD)
MAT/06 - PROBABILITY AND STATISTICS
Period
Primo semestre dal Oct 4, 2021 al Jan 28, 2022.
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. Conditional Expectation and Conditional Distribution. Martingale. Stopping theorem and convergence theorem.
2. Discrete-time Markov chains. Markov properties and transition probability. Irreducibility, aperiodicity. Stationary distributions. Reversible distributions.
3. Hitting times. One step analysis. Convergence to the stationary distribution. Law of large numbers for Markov chains. MCMC: Metropolis algorithm and Gibbs sampler.
4. Reducible Markov chains. Transient and recurring states. Probability of absorption.
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.
Bibliography
Examination Methods
To pass the exam, students must show:
- to have understood the theoretical notions, showing detailed knowledge of definitions and statements, as well as of some proofs;
- to be able to apply theory to problem solving.
The exam consists of a 180-minute written test which includes a theoretical part, with at least one proof required, and a part of exercises.
The assessment methods could change according to the academic rules
Type D and Type F activities
years | Modules | TAF | Teacher |
---|---|---|---|
1° 2° 3° | Python programming language | D |
Maurizio Boscaini
(Coordinator)
|
1° 2° 3° | SageMath | F |
Zsuzsanna Liptak
(Coordinator)
|
1° 2° 3° | History of Modern Physics 2 | D |
Francesca Monti
(Coordinator)
|
1° 2° 3° | History and Didactics of Geology | D |
Guido Gonzato
(Coordinator)
|
years | Modules | TAF | Teacher |
---|---|---|---|
1° 2° 3° | C Programming Language | D |
Sara Migliorini
(Coordinator)
|
1° 2° 3° | C++ Programming Language | D |
Federico Busato
(Coordinator)
|
1° 2° 3° | LaTeX Language | D |
Enrico Gregorio
(Coordinator)
|
years | Modules | 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
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 also via the Univr app.
Graduation
Documents
Title | Info File |
---|---|
1. Come scrivere una tesi | pdf, it, 31 KB, 29/07/21 |
2. How to write a thesis | pdf, it, 31 KB, 29/07/21 |
5. Regolamento tesi | pdf, it, 171 KB, 20/03/24 |
List of thesis 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 |
Attendance modes and venues
As stated in the Teaching Regulations , 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.
Part-time enrolment is permitted. Find out more on the Part-time enrolment possibilities page.
The course's teaching activities take place in the Science and Engineering area, which consists of the buildings of Ca‘ Vignal 1, Ca’ Vignal 2, Ca' Vignal 3 and Piramide, located in the Borgo Roma campus.
Lectures are held in the classrooms of Ca‘ Vignal 1, Ca’ Vignal 2 and Ca' Vignal 3, while practical exercises take place in the teaching laboratories dedicated to the various activities.
Career management
Student login and resources
Erasmus+ and other experiences abroad
Ongoing orientation for students
The committee has the task of guiding the students throughout their studies, guiding them in their choice of educational pathways, making them active participants in the educational process and helping to overcome any individual difficulties.
It is composed of professors Lidia Angeleri, Sisto Baldo, Marco Caliari, Paolo dai Pra, Francesca Mantese, and Nicola Sansonetto
To send an email to professors: name.surname@univr.it