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 sem. Oct 2, 2017 Jan 31, 2018
II sem. Mar 1, 2018 Jun 15, 2018
Exam sessions
Session From To
Sessione invernale d'esame 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 Estiva Lauree Magistrali Jul 19, 2018 Jul 19, 2018
Sessione Autunnale Lauree Magistrali Oct 18, 2018 Oct 18, 2018
Sessione Invernale Lauree Magistrali Mar 21, 2019 Mar 21, 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 Enrolment FAQs

Academic staff

B C D F G M O P Q R S V

Belussi Alberto

alberto.belussi@univr.it +39 045 802 7980
Foto,  February 9, 2017

Bloisi Domenico Daniele

domenico.bloisi@univr.it

Bombieri Nicola

nicola.bombieri@univr.it +39 045 802 7094

Bonacina Maria Paola

mariapaola.bonacina@univr.it +39 045 802 7046

Boscaini Maurizio

maurizio.boscaini@univr.it

Busato Federico

federico.busato@univr.it

Calanca Andrea

andrea.calanca@univr.it +39 045 802 7847

Carra Damiano

damiano.carra@univr.it +39 045 802 7059

Castellani Umberto

umberto.castellani@univr.it +39 045 802 7988

Cicalese Ferdinando

ferdinando.cicalese@univr.it +39 045 802 7969

Cristani Matteo

matteo.cristani@univr.it 045 802 7983

Cristani Marco

marco.cristani@univr.it +39 045 802 7841

Cubico Serena

serena.cubico@univr.it 045 802 8132

Dalla Preda Mila

mila.dallapreda@univr.it

Farinelli Alessandro

alessandro.farinelli@univr.it +39 045 802 7842

Favretto Giuseppe

giuseppe.favretto@univr.it +39 045 802 8749 - 8748

Fiorini Paolo

paolo.fiorini@univr.it 045 802 7963

Franco Giuditta

giuditta.franco@univr.it +39 045 802 7045

Fummi Franco

franco.fummi@univr.it 045 802 7994

Giachetti Andrea

andrea.giachetti@univr.it +39 045 8027998

Giacobazzi Roberto

roberto.giacobazzi@univr.it +39 045 802 7995

Manca Vincenzo

vincenzo.manca@univr.it 045 802 7981

Maris Bogdan Mihai

bogdan.maris@univr.it +39 045 802 7074

Masini Andrea

andrea.masini@univr.it 045 802 7922

Mastroeni Isabella

isabella.mastroeni@univr.it +39 045 802 7089

Menegaz Gloria

gloria.menegaz@univr.it +39 045 802 7024

Merro Massimo

massimo.merro@univr.it 045 802 7992

Muradore Riccardo

riccardo.muradore@univr.it +39 045 802 7835

Oliboni Barbara

barbara.oliboni@univr.it +39 045 802 7077

Pravadelli Graziano

graziano.pravadelli@univr.it +39 045 802 7081

Quaglia Davide

davide.quaglia@univr.it +39 045 802 7811

Rizzi Romeo

romeo.rizzi@univr.it +39 045 8027088

Romeo Alessandro

alessandro.romeo@univr.it +39 045 802 7974-7936; Lab: +39 045 802 7808

Segala Roberto

roberto.segala@univr.it 045 802 7997

Villa Tiziano

tiziano.villa@univr.it +39 045 802 7034

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:
ModulesCreditsTAFSSD
12
B
ING-INF/05
6
B
ING-INF/05
12
B
ING-INF/05
ModulesCreditsTAFSSD
6
B
INF/01
6
B
ING-INF/05
Other activitites
4
F
-
Final exam
24
E
-

1° Year

ModulesCreditsTAFSSD
12
B
ING-INF/05
6
B
ING-INF/05
12
B
ING-INF/05

2° Year

ModulesCreditsTAFSSD
6
B
INF/01
6
B
ING-INF/05
Other activitites
4
F
-
Final exam
24
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°2 courses to be chosen among the following
6
C
INF/01
6
C
INF/01
6
C
INF/01
Between the years: 1°- 2°

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

4S02803

Coordinatore

Marco Cristani

Credits

6

Language

Italian

Scientific Disciplinary Sector (SSD)

ING-INF/05 - INFORMATION PROCESSING SYSTEMS

Period

II sem. dal Mar 1, 2018 al Jun 15, 2018.

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

The course program can be divided into two parts, the methodological and the applicative one, which will go hand in hand with the lessons.

Methodologies
--- Introduction: classification systems, types of classification, applications
>> SUPERVISED LEARNING <<
--- Bayes decision theory, risk minimization
--- linear, non-linear classifiers and discriminant functions
--- Selection and extraction of features, Principal Component Analysis, Fisher Linear Discriminant Analysis
--- Parameter Estimation: Maximum Maximum Likelihood, MAP, Bayesian
--- Single Gaussian estimation and mixture of Gaussians: Expectation-Maximization algorithm and variational approximations (Mean Field)
--- Non-parametric methods for training a classifier: Parzen Windows and K-Nearest-Neighbor
--- Monte Carlo methods for dynamic density estimation, Particle Filtering
--- Markov models and Hidden Markov Models

>> NON-SUPERVISED CLASSIFICATION <<
--- Partitional methods (k-means and x-means), hierarchical (single-double linkage)
--- Internal and external validation criteria


Applications
-- Binary and multiclass classification on real benchmarks
-- Face recognition
-- Tracking


Textbooks:
- Richard O. Duda, Peter E. Hart, and David G. Stork. 2000. Pattern Classification (2nd Edition). Wiley-Interscience.
- Christopher M. Bishop. 2006. Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag New York, Inc., Secaucus, NJ, USA.

Reference texts
Author Title Publishing house Year ISBN Notes
R. Duda, P. Hart, D. Stork Pattern Classification Wiley 2001
C.M. Bishop Pattern Recognition and Machine Learning Springer 2006

Examination Methods

Examination method is oral; the required content will be those seen during the lessons, as indicated by the course program. In particular, when necessary, a formal demonstration of a procedure will be requested. In all cases, the questions will address a classification problem where the student will have to suggest the most suitable technique for the case, formally demonstrating the choice. The final vote will be built depending on the student's proposed solution to the question (20 points total), and the formal accuracy with which the solution is presented (10 points).

Bibliography

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.

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

theses proposals Research area
Analisi ed identificazione automatica del tono/volume della voce AI, Robotics & Automatic Control - AI, Robotics & Automatic Control
Analisi e percezione dei segnali biometrici per l'interazione con robot AI, Robotics & Automatic Control - AI, Robotics & Automatic Control
Integrazione del simulatore del robot Nao con Oculus Rift AI, Robotics & Automatic Control - AI, Robotics & Automatic Control
BS or MS theses in automated reasoning Computing Methodologies - ARTIFICIAL INTELLIGENCE
Sviluppo sistemi di scansione 3D Computing Methodologies - COMPUTER GRAPHICS
Sviluppo sistemi di scansione 3D Computing Methodologies - IMAGE PROCESSING AND COMPUTER VISION
Dati geografici Information Systems - INFORMATION SYSTEMS APPLICATIONS
Analisi ed identificazione automatica del tono/volume della voce Robotics - Robotics
Analisi e percezione dei segnali biometrici per l'interazione con robot Robotics - Robotics
Integrazione del simulatore del robot Nao con Oculus Rift Robotics - Robotics
BS or MS theses in automated reasoning Theory of computation - Logic
BS or MS theses in automated reasoning Theory of computation - Semantics and reasoning
Proposte di tesi/collaborazione/stage in Intelligenza Artificiale Applicata Various topics
Proposte di Tesi/Stage/Progetto nell'ambito delle basi di dati/sistemi informativi Various topics

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.

Career management


Area riservata studenti