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.

A.A. 2020/2021

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 semestre Oct 1, 2020 Jan 29, 2021
II semestre Mar 1, 2021 Jun 11, 2021
Exam sessions
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
Degree sessions
Session From To
Sessione Estiva Jul 15, 2021 Jul 15, 2021
Sessione Autunnale Oct 15, 2021 Oct 15, 2021
Sessione Invernale Mar 15, 2022 Mar 15, 2022
Holidays
Period From To
Festa dell'Immacolata Dec 8, 2020 Dec 8, 2020
Vacanze Natalizie Dec 24, 2020 Jan 3, 2021
Epifania Jan 6, 2021 Jan 6, 2021
Vacanze Pasquali Apr 2, 2021 Apr 5, 2021
Festa del Santo Patrono May 21, 2021 May 21, 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.

Exam calendar

Should you have any doubts or questions, please check the Enrolment FAQs

Academic staff

A B C D F G L M P S

Accordini Simone

simone.accordini@univr.it +39 045 8027657

Baruffi Maria Caterina

mariacaterina.baruffi@univr.it

Bicego Manuele

manuele.bicego@univr.it +39 045 802 7072

Bombieri Cristina

cristina.bombieri@univr.it 045-8027209

Bombieri Nicola

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

Cicalese Ferdinando

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

Combi Carlo

carlo.combi@univr.it 045 802 7985

Constantin Gabriela

gabriela.constantin@univr.it 045-8027102

Daducci Alessandro

alessandro.daducci@univr.it +39 045 8027025

Delledonne Massimo

massimo.delledonne@univr.it 045 802 7962; Lab: 045 802 7058

Franco Giuditta

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

Fummi Franco

franco.fummi@univr.it 045 802 7994

Giacobazzi Roberto

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

Giugno Rosalba

rosalba.giugno@univr.it 0458027066

Laudanna Carlo

carlo.laudanna@univr.it 045-8027689

Liptak Zsuzsanna

zsuzsanna.liptak@univr.it +39 045 802 7032

Malerba Giovanni

giovanni.malerba@univr.it 045/8027685

Marcon Alessandro

alessandro.marcon@univr.it +39 045 802 7668

Maris Bogdan Mihai

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

Perduca Massimiliano

massimiliano.perduca@univr.it +39 045 802 7984

Sala Pietro

pietro.sala@univr.it 0458027850

Salvagno Gian Luca

gianluca.salvagno@univr.it 045 8124308-0456449264

Spoto Nicola Fausto

fausto.spoto@univr.it +39 045 8027940

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.

ModulesCreditsTAFSSD
Final exam
24
E
-

2° Year

ModulesCreditsTAFSSD
Final exam
24
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°
English b2 level
4
F
-
Between the years: 1°- 2°
Other activities
2
F
-
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

4S004551

Credits

6

Coordinatore

Manuele Bicego

Scientific Disciplinary Sector (SSD)

ING-INF/05 - INFORMATION PROCESSING SYSTEMS

Language

English

The teaching is organized as follows:

Teoria

Credits

4

Period

I semestre

Academic staff

Manuele Bicego

Laboratorio

Credits

2

Period

I semestre

Academic staff

Manuele Bicego

Learning outcomes

The course is aimed at providing the theoretical and applicative basis of Pattern Recognition techniques for the computational analysis of biological objects with a complex structure (such as graphs, sequences, networks, strings and so on). In particular, the course introduces and discusses the most important computational techniques for the analysis of structured data, with particular emphasis on the representation and on the generative and discriminative approaches. Knowledge and understanding: At the end of the course, the student has to demonstrate to be able to apply to real data the methodologies for recognition of complex data, by developing a Pattern Recognition system. Applying knowledge and understanding: a) Representation of biological data with complex structure b) Classification of biological data with complex structure c) Clustering of biological data with complex structure Making judgements: At the end of the course, the student should demonstrate to be able to propose in an autonomous way efficient solutions for a given biomedical and bioinformatics domain, being able to identify critical issues linked to complex bioinformatics problems. Communication: At the end of the course, the tudent should demonstrate to be able to interact with colleagues in work groups. Lifelong learning skills: At the end of the course, the student should demonstrate to be able to learn and autonomously apply novel methodologies for facing bioinformatics and clinical problems. In particular, the student should demonstrate to be able to analyse a biological problem, involving complex and structured biological data, from a Pattern Recognition perspective; he will also have the skills needed to study, invent, develop and implement the different components of a Pattern Recognition System for biological structured data. The student will also be able to autonomously proceed with further Pattern Recognition studies.

Program

CHAPTER 1 Basic Pattern Recognition concepts and introduction to structured data
CHAPTER 2. Representation of structured data
- Advanced dimensionality reduction techniques
- The Bag of words representation
- The dissimilarity-based representation
CHAPTER 3. Models for structured data
- Generative models
- Bayes Networks
- Learning and inference
CHAPTER 4. Kernels for structured data
- Support Vector Machines e kernel
- Kernels for structured data
CHAPTER 5. Advances Learning paradigms

The course also contains a lab part, where algorithms seen during the theory part will be implemented and deeply analysed

Examination Methods

The exam is aimed at the verification of the following skills:
- capability of clearly and concisely describe the different components of a Pattern Recognition System for structured data
- capability of analise, understand and describe a Pattern Recognition system (or a given part of it) relative to a biological problem which involves structured data

The exam consists of two parts
i) a written exam containing questions on topics presented during the course (15 points available). The written part is passed is the grade is greater or equal to 9.
ii) an oral presentation of a scientific paper published in relevant bioinformatics journals or conferences on a given argument (decided during the course). The paper is chosen by the candidate and approved by the instructor (15 points available).

The two parts of the exam can be passed separately: the final grade is the sum of the two grades.
The total exam is passed if the final grade is greater or equal to 18. Each evaluation is maintained valid for the whole academic year.

Teaching materials

Type D and Type F activities

I semestre From 10/1/20 To 1/29/21
years Modules TAF Teacher
1° 2° Matlab-Simulink programming D Bogdan Mihai Maris (Coordinatore)
II semestre From 3/1/21 To 6/11/21
years Modules TAF Teacher
1° 2° Introduction to 3D printing D Franco Fummi (Coordinatore)
1° 2° Python programming language D Vittoria Cozza (Coordinatore)
1° 2° HW components design on FPGA D Franco Fummi (Coordinatore)
1° 2° Rapid prototyping on Arduino D Franco Fummi (Coordinatore)
1° 2° Protection of intangible assets (SW and invention)between industrial law and copyright D Roberto Giacobazzi (Coordinatore)
List of courses with unassigned period
years Modules TAF Teacher
1° 2° The fashion lab (1 ECTS) D Maria Caterina Baruffi (Coordinatore)
1° 2° 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.

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.

Gestione carriere


Graduation


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.