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. | Jul 16, 2020 | Jul 16, 2020 |
| Sessione Autunnale. | Oct 15, 2020 | Oct 15, 2020 |
| Sessione Invernale. | Mar 18, 2021 | Mar 18, 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.
Should you have any doubts or questions, please check the Enrolment FAQs
Academic staff
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 |
|---|
| 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.
Machine Learning & Pattern Recognition (2019/2020)
Teaching code
4S02803
Credits
6
Coordinatore
Language
Italian
Scientific Disciplinary Sector (SSD)
ING-INF/05 - INFORMATION PROCESSING SYSTEMS
The teaching is organized as follows:
Teoria
Laboratorio
Learning outcomes
The course aims to provide: i) methodological principles underlying the classification; ii) feature selection and extraction techniques; iii) algorithms for supervised and unsupervised learning; parametric and non-parametric parameter estimation; iv) cross-validation techniques for the validation of classifiers. At the end of the course the student should be able to understand if a classification problem can be solved with some existing technology and, in that case, the type of machine learning algorithm that has to be used for the training. Furthermore, the student must demonstrate: i) to understand what kind of characteristics or patterns should be extracted from the raw data coming from a sensor; ii) to understand what kind of classifier should be used in relation with the encountered problem: iii) to understand the complexity of the recognition problem in computational terms; iv) to produce software that recognizes real data; v) be able to use other people's code and modify it adapting it to the problem under examination. This knowledge will allow the student to understand: i) that fit measures guarantee an effective classifier after the phase of his training; ii) what are the techniques for validating the results of a classifier. At the end of the course the student will be able to understand a machine learning or pattern recognition paper.
Program
Introduction: what, what it serves for, systens and applications
Bayes decision theory
Parameter estimation and non-parametric methods
Linear and nonlinear classifiers and discriminant functions
Linear transformations, Fisher method, feature estraction and selection, Principal Component Analysis
Gaussian mixtures and Expectation-Maximization
Kernel methods and Support Vector Machines
Artificial neural networks
Unsupervised learning and clustering
Hidden Markov Models
Examination Methods
Project with report and oral presentation
To be executed with 2 people, 3 people max for more complex projects (to agree with professor)
Oral presentation will also be focused on verifying the knowledge of the course contents
Type D and Type F activities
| years | Modules | TAF | Teacher |
|---|---|---|---|
| 1° 2° | The fashion lab (1 ECTS) | D | Not yet assigned |
| years | Modules | TAF | Teacher |
|---|---|---|---|
| 1° 2° | Python programming language | D |
Maurizio Boscaini
(Coordinatore)
|
| years | Modules | TAF | Teacher |
|---|---|---|---|
| 1° 2° | CyberPhysical Laboratory | D |
Andrea Calanca
(Coordinatore)
|
| 1° 2° | C++ Programming Language | D |
Federico Busato
(Coordinatore)
|
| 1° 2° | Matlab-Simulink programming | D |
Bogdan Mihai Maris
(Coordinatore)
|
| years | Modules | TAF | Teacher |
|---|---|---|---|
| 1° 2° | Corso Europrogettazione | D | Not yet assigned |
| 1° 2° | The course provides an introduction to blockchain technology. It focuses on the technology behind Bitcoin, Ethereum, Tendermint and Hotmoka. | D |
Matteo Cristani
|
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
List of theses and work experience proposals
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.
