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. 2021/2022

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
Primo semestre Oct 4, 2021 Jan 28, 2022
Secondo semestre Mar 7, 2022 Jun 10, 2022
Exam sessions
Session From To
Sessione invernale d'esame Jan 31, 2022 Mar 4, 2022
Sessione estiva d'esame Jun 13, 2022 Jul 29, 2022
Sessione autunnale d'esame Sep 1, 2022 Sep 29, 2022
Degree sessions
Session From To
Sessione Estiva Jul 15, 2022 Jul 15, 2022
Sessione Autunnale Oct 14, 2022 Oct 14, 2022
Sessione Invernale Mar 14, 2023 Mar 14, 2023
Holidays
Period From To
Festa di Tutti i Santi Nov 1, 2021 Nov 1, 2021
Festa dell'Immacolata Concezione Dec 8, 2021 Dec 8, 2021
Festività natalizie Dec 24, 2021 Jan 2, 2022
Festa dell'Epifania Jan 6, 2022 Jan 7, 2022
Festività pasquali Apr 15, 2022 Apr 19, 2022
Festa della Liberazione Apr 25, 2022 Apr 25, 2022
Festività Santo Patrono di Verona May 21, 2022 May 21, 2022
Festa della Repubblica Jun 2, 2022 Jun 2, 2022
Chiusura estiva Aug 15, 2022 Aug 20, 2022

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

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

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

Fiorini Paolo

paolo.fiorini@univr.it 045 802 7963

Fratea Caterina

caterina.fratea@univr.it 045 802 8858

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

Marzola Pasquina

pasquina.marzola@univr.it 045 802 7816 (ufficio); 045 802 7614 (laboratorio)

Menegaz Gloria

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

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

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
Further linguistic skills (C1 English suggested)
3
F
-
Stages
3
F
-
Final exam
24
E
-
Modules Credits TAF SSD
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

4S004553

Teacher

Pietro Sala

Coordinatore

Pietro Sala

Credits

6

Scientific Disciplinary Sector (SSD)

INF/01 - INFORMATICS

Language

English en

Period

Secondo semestre dal Mar 7, 2022 al Jun 10, 2022.

Learning outcomes

Knowledge and understanding The course aims to introduce principles that form the foundations of the Decision Support System together with case-studies of their real-world applications, with particular focus on their use in Biomedical domain. In particular, the purpose of the course consists of providing advanced knowledge on the techniques and principles involved in managing and manipulating very large databases (with specific examples borrowed from the biomedical domain). Moreover, the course will provide the theretical and practical foundations of the main data mining techniques used in clinical domains. Applying knowledge and understanding During the course students will aquire the following competences: - they will be able to choose and use the appropriate components in order to provide solution for supporting decision to the medical staff; - they will be able torealize complex operations of Extraction, Transformation, and Loading (ETL) on several clinical data types coming from different sources (Relational Databases, API, Websites, and so on) and encoded in both structure (relational tables) and semi-structured (XML) fashion; -they will be able to model and realize OLAP (On-Line Analytical Processing) solutions for supportuing decisions in a Biomedical context; -they will be able to use or adapt advanced data-mining techniques (Approximate Functional Dependencies, Association Rules, Entropy-based Classifiers, and so on) for extracting knowledge from large amounts of data. Making judgements Students will develop the required skills in order to be autonomous in the following tasks: - choose and apply data mining techniques for extracting medical knowledge from large amount of data; - choose the appropriate graphical/interactive representations for represent specific clinical information. Communication skills The student will learn how to address the correct priorities to the informations that must be reported to the end-user according to his needs and the language of his domain. Learning skills The students will be introduced to the main algorithms and techniques used in the clinical data mining field, together with the description of the factors that affect their efficiency and effectiveness. This knowledge will be the basis for comprehend more specific techniques adopted nowadays for data mining for clinical domain. Moreover, the student will be able to choose autonomously the data mining techinque for answering a given quesry of the end-user. Finally, he will be able to evaluate the performance and the accuracy of the proposed solution.

Program

Functional Dependencies (FD):
concepts and applications of FDs, forcing and verifying FDs in PostgreSQL

Approximate Functional Dependencies (AFD):
introducing approximation in FDs as confidence measure. Clinical knowledge extraction using AFD: examples. AFD analysis in the biomedical context.

Algorithms for extracting AFDs:
minimal AFDs: definition, semantics and analysis. Theoretical Lower Bounds on the number of minimal AFD: the curse of cardinality. Basic algorthm for extracting minimal AFD. Compact representations of
sets of extracte AFDs. Randomized algorithms for extracting minimal AFDs:
theory and implementation.

Approximation in presence of measures:
Delta Functional Dependencies (DFDs) : definition, application, and verification. Analysis of DFDs extracted from the biomedical domain. Approximated DFDs
(ADFD):
definition, applications and analysis in the biomedical domain (examples). Algorithm for verifying single ADFD restricted to the case of 2 measures (2ADFD):
complexity, implementation. Extraction of minimal 2ADFD from clinical data.

Association Rules (ARs):
definition, examples in the biomedical domain. Extraction of di AR: support and confidence. Theoretical analysis: the curse of cardinality. Frequent Itemsets (FIs): definition, role in the extraction
of ARs, and algorithm for vandidates generation. ARs extraction from sets of FIs. Sets of FIs: minimal sets, closed sets.
Strategies for exploring FIs lattices. Alternatives to standard extraction algorithm using specific data structures (hash trees, FP-trees). Evaluation of association patterns: drawbacks of the support/confidence framework. Examples of paradoxes. alternative measures for association pattern analysis:
definition and examples.

Extraction Transformation and Loading (ETL):
definition, functions, role inside a data warehouse, data flows. Basic entities of ETL procedures and how they work: Job, Transformations, Job, Step, Transformation Step. Conceptual modelling of ETL procedures in Business Process Model and Notation (BPMN). Modelling examples: case studies. Embedding external procedures into ETL procedures: comunication, staging and managing of errors. API (Application Programming Interface) usage inside ETL procedures. Short description of XPATH constructs and how to use them. Screen scraping of websites in ETL procedures by using XPATH. Using Business Intellingence tools to realize ETL procedures.

Entropy-based classifiers:
introduction to the concept of Entropy. Decision Trees in the biomedical context. The Iterative Dichotomiser 3 (ID3) classifier: algorithm, examples and implementation. Measures discretization. Using ID3 for discretizing measures:
problems, modification and implementation. Temporal analysis application to adverse drug reactions.

Reporting and OLAP (Online Analytical Processing):
Interactive reporting systems: querying the clinical databases, parametrization of the reports. Dynamic retrieval of report information by using ETAL transformation. Modelling analysis using OLAP cubes and theri implementation: case studies. Using Business Intellingence tools to realize dynamic/interactive reports and OLAP cubes

SUGGESTED TEXTS:

DJ Hand, H Mannila, P Smyth
Principles of data mining
MIT Press Cambridge, MA, USA ©2001
ISBN:0-262-08290-X 9780262082907

Roland Bouman, Jos van Dongen
Pentaho Solutions: Business Intelligence and Data Warehousing with Pentaho and MySQL
Wiley Publishing, Inc.
ISBN: 978-0-470-48432-6
648 pages
September 2009

Fulton, Hal and Olsen, Russ
The ruby way: solutions and techniques in ruby programming, third edition
Addison-Wesley Professional ©2014
ISBN:0-321-71463-6

COURSE MATERIAL:

class slides;
example data (in .csv format) for completing the exercises proposed during classes;
implementation of the procedures introduced during the course.

Bibliografia

Visualizza la bibliografia con Leganto, strumento che il Sistema Bibliotecario mette a disposizione per recuperare i testi in programma d'esame in modo semplice e innovativo.

Examination Methods

The exam modality aims to verify the autonomy and the skills of the student in applying the concepts provided during the course for realizing Decision Support Systems.
The exam consists of an interview on the implementation
of two projects assigned during classes, one for each macro-topic of the course:
1) Data Mining;
2) OLAP Analysis.
The two projects must be realized as a team or as an individual. Moreover, a necessary but not sufficient condition for passing the exam is that both the implementations of the projects must be complete. In particular, each project will be evaluated on a scale going from 1 to 15 included, the final grade is given by the sum of the two individual project grades.

There is no difference in the exam modality among students that attended the course and students that did not.

Type D and Type F activities

Le attività formative di tipologia D o F comprendono gli insegnamenti impartiti presso l'Università di Verona o periodi di stage/tirocinio professionale.
Nella scelta delle attività di tipo D, gli studenti dovranno tener presente che in sede di approvazione si terrà conto della coerenza delle loro scelte con il progetto formativo del loro piano di studio e dell'adeguatezza delle motivazioni eventualmente fornite. Dal 1° dicembre 2021 al 27 febbraio 2022 e dal 2 maggio 2022 al 15 luglio 2022, tramite il presente modulo gli studenti possono richiedere l'inserimento di attività didattiche in TAF D ed F che non possono inserire autonomamente nel proprio piano di studi tramite la procedura on-line.

COMPETENZE LINGUISTICHE - dal 1° ottobre 2021 (Delibera del Consiglio della Scuola di Scienze e Ingegneria del 30 marzo 2021) per gli immatricolati dall'A.A. 2021/2022

  • Lingua inglese: vengono riconosciuti automaticamente 3 CFU per ogni livello di competenza superiore a quello richiesto dal corso di studio (se non già riconosciuto nel ciclo di studi precedente).
  • Altre lingue e italiano per stranieri: vengono riconosciuti automaticamente 3 CFU per ogni livello di competenza a partire da A2 (se non già riconosciuto nel ciclo di studi precedente).
Tali CFU saranno riconosciuti, fino ad un massimo di 6 CFU complessivi, di tipologia F se il piano didattico lo consente, oppure di tipologia D.
Ulteriori crediti a scelta per conoscenze linguistiche potranno essere riconosciuti solo se coerenti con il progetto formativo dello studente e se adeguatamente motivati.

COMPETENZE TRASVERSALI
Scopri i percorsi formativi promossi dal  Teaching and learning centre dell'Ateneo, destinati agli studenti iscritti ai corsi di laurea, volti alla promozione delle competenze trasversali:

1° periodo lezioni (1A) From 9/16/21 To 10/30/21
years Modules TAF Teacher
The fashion lab (1 ECTS) D Caterina Fratea (Coordinatore)
Primo semestre From 10/4/21 To 1/28/22
years Modules TAF Teacher
1° 2° Data Analysis for Biomedical Sciences D Gloria Menegaz (Coordinatore)
1° 2° Introduction to Robotics to students of scientific courses. D Paolo Fiorini (Coordinatore)
1° 2° Matlab-Simulink programming D Bogdan Mihai Maris (Coordinatore)
Modules borrowed from the Faculty of Scienze matematiche fisiche e naturali
1° periodo lezioni (1B) From 11/5/21 To 12/16/21
years Modules TAF Teacher
The fashion lab (1 ECTS) D Caterina Fratea (Coordinatore)
Secondo semestre From 3/7/22 To 6/10/22
years Modules TAF Teacher
1° 2° Introduction to Robotics to students of scientific courses. D Paolo Fiorini (Coordinatore)
1° 2° Introduction to 3D printing D Franco Fummi (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° Python programming language D 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.

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.