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, 2020 | Jan 29, 2021 |
| II semestre | Mar 1, 2021 | Jun 11, 2021 |
| 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 |
| Period | From | To |
|---|---|---|
| Festa dell'Immacolata | Dec 8, 2020 | Dec 8, 2020 |
| Vacanze Natalizie | Dec 24, 2020 | Jan 3, 2021 |
| Vacanze Pasquali | Apr 2, 2021 | Apr 5, 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.
Should you have any doubts or questions, please check the Enrolment FAQs
Academic staff
Bullini Orlandi Ludovico
ludovico.bulliniorlandi@univr.it
045 802 8095
marco.carradore@univr.it
mariano.ceccato@univr.it
francescogiuseppe.cordoni@univr.it
mila.dallapreda@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.
| Modules | Credits | TAF | SSD |
|---|
1° Year
| Modules | Credits | TAF | SSD |
|---|
2° Year
| Modules | Credits | TAF | SSD |
|---|
| Modules | Credits | TAF | SSD |
|---|
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.
Scienza dei sistemi complessi e fisica sociale (2020/2021)
Teaching code
4S009082
Teacher
Coordinatore
Credits
6
Language
English
Scientific Disciplinary Sector (SSD)
FIS/02 - THEORETICAL PHYSICS, MATHEMATICAL MODELS AND METHODS
Period
II semestre dal Mar 1, 2021 al Jun 11, 2021.
Learning outcomes
The aim of the course is to provide the student with the skills of theoretical physics and mathematical physics methods for the modeling and characterization of large sets of data, time series, time sequences, and hierarchical structures in aggregation. Students will also be provided with the methods of mathematical physics for the study of correlation, causation, and aggregation relationships in complex social systems.
At the end of the course the student has to show to have acquired the following skills:
● ability to develop formal models for the qualitative and quantitative analysis of databases, time series, and dynamics of complex systems in interaction for the detection of causal relationships, correlation structures, and forecasting schemes.
Program
Review of thermodynamics and statistical mechanics. System theory. Examples and applications to natural and social systems. Dynamical systems. Correlation, causation, stationarity. Transformations: time domain, linear regression, frequency domain, principal components. Linear dynamical systems. Diagonalization of the dynamics, normal modes, fixed points. Coupled systems. Control of dynamical systems. Nonlinear dynamical systems. Bifurcations, hysteresis, catastrophes. Examples and applications. Linearization. Limit cycles. Control and stabilization. Spatial systems. Agent models. Review of probability and statistics. Distributions: uniform, exponential, Gaussian. Measures of non-Gaussianity. Outliers and rare events, heavy-tailed distributions. Power laws. Power laws in social systems. Complex systems. Phase transitions and critical phenomena. Self-organized criticality. Emergence. Observation and inference. Sampling and inverse problems.
Examination Methods
Oral exam, possibly including the discussion of an essay on a topic covered in the course.
Type D and Type F activities
| years | Modules | TAF | Teacher |
|---|---|---|---|
| 1° | The course provides an introduction to blockchain technology. It focuses on the technology behind Bitcoin, Ethereum, Tendermint and Hotmoka. | D |
Nicola Fausto Spoto
(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.
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.
Graduation
Attachments
| Title | Info File |
|---|---|
Regolamento esame finale | Final exam regulation
|
387 KB, 27/04/22 |
List of theses and work experience proposals
| theses proposals | Research area |
|---|---|
| Domain Adaptation | Computer Science and Informatics: Informatics and information systems, computer science, scientific computing, intelligent systems - Computer graphics, computer vision, multi media, computer games |
| Domain Adaptation | Computer Science and Informatics: Informatics and information systems, computer science, scientific computing, intelligent systems - Machine learning, statistical data processing and applications using signal processing (e.g. speech, image, video) |
| Domain Adaptation | Computing Methodologies - IMAGE PROCESSING AND COMPUTER VISION |
| Domain Adaptation | Computing methodologies - Machine learning |
Attendance
As stated in point 25 of the Teaching Regulations for the A.Y. 2021/2022, attendance at the course of study is not mandatory.Please refer to the Crisis Unit's latest updates for the mode of teaching.
