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
Semester 1 Oct 2, 2023 Jan 26, 2024
Semester 2 Mar 4, 2024 Jun 14, 2024
Exam sessions
Session From To
Winter exam session Jan 29, 2024 Mar 1, 2024
Summer exam session Jun 17, 2024 Jul 31, 2024
Autumn exam session Sep 2, 2024 Sep 30, 2024
Degree sessions
Session From To
Summer graduation session Jul 19, 2024 Jul 19, 2024
Autumn graduation session Oct 21, 2024 Oct 21, 2024
Winter graduation session Mar 27, 2025 Mar 27, 2025
Holidays
Period From To
Festa di Ognissanti Nov 1, 2023 Nov 1, 2023
Festa dell'Immacolata Dec 8, 2023 Dec 8, 2023
Vacanze di Natale Dec 24, 2023 Jan 7, 2024
Festività pasquali Mar 29, 2024 Apr 1, 2024
Ponte della Festa della Liberazione Apr 25, 2024 Apr 26, 2024
Festa del Lavoro May 1, 2024 May 1, 2024
Festività del Santo Patrono: San Zeno May 21, 2024 May 21, 2024
Festa della Repubblica Jun 2, 2024 Jun 2, 2024
Vacanze estive Aug 12, 2024 Aug 17, 2024

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 Enrollment FAQs

Academic staff

B C D F G M O P Q R S T Z

Belussi Alberto

symbol email alberto.belussi@univr.it symbol phone-number +39 045 802 7980

Bombieri Nicola

symbol email nicola.bombieri@univr.it symbol phone-number +39 045 802 7094

Bonacina Maria Paola

symbol email mariapaola.bonacina@univr.it symbol phone-number +39 045 802 7046

Carra Damiano

symbol email damiano.carra@univr.it symbol phone-number +39 045 802 7059

Castellani Umberto

symbol email umberto.castellani@univr.it symbol phone-number +39 045 802 7988

Ceccato Mariano

symbol email mariano.ceccato@univr.it

Cicalese Ferdinando

symbol email ferdinando.cicalese@univr.it symbol phone-number +39 045 802 7969

Combi Carlo

symbol email carlo.combi@univr.it symbol phone-number +39 045 802 7985

Cristani Marco

symbol email marco.cristani@univr.it symbol phone-number +39 045 802 7841

Cubico Serena

symbol email serena.cubico@univr.it symbol phone-number 045 802 8132

Dalla Preda Mila

symbol email mila.dallapreda@univr.it

Di Pierro Alessandra

symbol email alessandra.dipierro@univr.it symbol phone-number +39 045 802 7971

Farinelli Alessandro

symbol email alessandro.farinelli@univr.it symbol phone-number +39 045 802 7842

Fummi Franco

symbol email franco.fummi@univr.it symbol phone-number 045 802 7994

Gregorio Enrico

symbol email Enrico.Gregorio@univr.it symbol phone-number +39 045 802 7937

Mastroeni Isabella

symbol email isabella.mastroeni@univr.it symbol phone-number +39 045 802 7089

Merro Massimo

symbol email massimo.merro@univr.it symbol phone-number +39 045 802 7992

Migliorini Sara

symbol email sara.migliorini@univr.it symbol phone-number +39 045 802 7908

Oliboni Barbara

symbol email barbara.oliboni@univr.it symbol phone-number +39 045 802 7077

Paci Federica Maria Francesca

symbol email federicamariafrancesca.paci@univr.it symbol phone-number +39 045 802 7909

Petrakis Iosif

symbol email iosif.petrakis@univr.it symbol phone-number +39 045 802 7973

Pianezzi Daniela

symbol email daniela.pianezzi@univr.it

Posenato Roberto

symbol email roberto.posenato@univr.it

Quaglia Davide

symbol email davide.quaglia@univr.it symbol phone-number +39 045 802 7811

Rizzi Romeo

symbol email romeo.rizzi@univr.it symbol phone-number +39 045 802 7088

Sala Pietro

symbol email pietro.sala@univr.it symbol phone-number +39 045 802 7850

Segala Roberto

symbol email roberto.segala@univr.it symbol phone-number +39 045 802 7997

Tomazzoli Claudio

symbol email claudio.tomazzoli@univr.it

Zorzi Margherita

symbol email margherita.zorzi@univr.it symbol phone-number +39 045 802 7045

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 enrollment year.

CURRICULUM TIPO:

2° Year   activated in the A.Y. 2024/2025

ModulesCreditsTAFSSD
Final exam
24
E
-
activated in the A.Y. 2024/2025
ModulesCreditsTAFSSD
Final exam
24
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°
Between the years: 1°- 2°
English B2
3
F
-
Between the years: 1°- 2°
Further activities
3
F
-

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.




S Placements in companies, public or private institutions and professional associations

Teaching code

4S008911

Credits

6

Coordinator

Pietro Sala

Language

Italian

Scientific Disciplinary Sector (SSD)

INF/01 - INFORMATICS

Courses Single

Authorized

The teaching is organized as follows:

Teoria

Credits

5

Period

Semester 1

Academic staff

Pietro Sala

Laboratorio

Credits

1

Period

Semester 1

Academic staff

Pietro Sala

Learning objectives

The corse aims to provide the theoretical and practical foundations for integrating data from, possibly, heterogeneous sources and the subsequent phase of extraction of summary information/knowledge. By completing the course, the students will be able to tackle complex data mining problems by designing and implementing a full pipeline that allows its user to integrate the necessary data sources, select and apply the adequate data mining techniques for solving a specific data mining problem, and evaluate its performances. Given a data mining problem, coming from a real-world domain ranging from industry to healthcare, the course enables the students to design, apply and test original solutions or or modifications of existing ones, for solving it and evaluate the feasibility of the proposed solution in a real environment.

Prerequisites and basic notions

Good programming skills,
good database skills especially querying and
manipulating data.

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. Knowledge extraction using AFD: examples. AFD analysis.
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 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 applications.
Reporting and OLAP (Online Analytical Processing):
Interactive reporting systems: querying large databases, parametrization of the reports. Dynamic retrieval of report information by using ETL transformations. Modelling analysis using OLAP cubes and their implementation: case studies. Using Business Intellingence tools to realize dynamic/interactive reports and OLAP cubes
Distributed Data Mining:
elements of distributed computing, split a data mining problem for solving it in a distributed fashion,
model and implement a ditributed system for data mining. How to use NoSQL databases for
distributed computations.
Probabilistic Analysis of Processes:
Qualitative analysis of a process using process mining and process discovery
techniques. Extraction and trasformation of processes into
probabilistic models (Markov Chains, Markov Decision Processes).
Tools for probabilistic analysis of systems (PRISM model checker).
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
The elements of statistical learning. Data mining, inference, and prediction.
T. Hastie, R. Tibshirani, J. Friedman.
2009 Springer
COURSE MATERIAL:
class slides;
example data (in .csv format) for completing the exercises proposed during classes;
implementation of the procedures introduced during the course;
Jupyter notebooks and docker containers for easily run the algorithm explained during the lectures.

Didactic methods

During the lecture, which will not be recorded,
a more in-depth explanation of the aforementioned topics
will be given by means of examples and
exercises that the lecturer will explain and comment.
Moreover, after the explanation, the lecturer
is available for helping the students with the exercises.
The lecture will be given in the lab, and students are encouraged, if possible, to attend in person.

Learning assessment procedures

The exam modality aims to verify the autonomy and the skills of the student in applying the concepts provided during the course for realizing a full end-to-end pipeline for a given Data Mining problem.
The exam consists of an interview on the implementation
of two projects assigned during classes, one for each macro-topic of the course:
1) ETL and OLAP Analysis
2) Data Mining;
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.

Students with disabilities or specific learning disorders (SLD), who intend to request the adaptation of the exam, must follow the instructions given HERE

Evaluation criteria

The projects will be evaluated according to the following criteria:
requirements fulfillment;
soundness, completeness, and clarity of the code and the documentation;
consistent application of the methodologies explained during the lectures.

Criteria for the composition of the final grade

requirements fulfillment (10 points);
soundness, completeness, and clarity of the code and the documentation (10 points);
consistent application of the methodologies explained during the lectures (10 points).

Exam language

Italiano (English if requested by the student)

Type D and Type F activities

Le attività formative di tipologia D sono a scelta dello studente, quelle di tipologia F sono ulteriori conoscenze utili all’inserimento nel mondo del lavoro (tirocini, competenze trasversali, project works, ecc.). In base al Regolamento Didattico del Corso, alcune attività possono essere scelte e inserite autonomamente a libretto, altre devono essere approvate da apposita commissione per verificarne la coerenza con il piano di studio. Le attività formative di tipologia D o F possono essere ricoperte dalle seguenti attività.

1. Insegnamenti impartiti presso l'Università di Verona

Comprendono gli insegnamenti sotto riportati e/o nel Catalogo degli insegnamenti (che può essere filtrato anche per lingua di erogazione tramite la Ricerca avanzata).

Modalità di inserimento a libretto: se l'insegnamento è compreso tra quelli sottoelencati, lo studente può inserirlo autonomamente durante il periodo in cui il piano di studi è aperto; in caso contrario, lo studente deve fare richiesta alla Segreteria, inviando a carriere.scienze@ateneo.univr.it il modulo nel periodo indicato.

2. Attestato o equipollenza linguistica CLA

Oltre a quelle richieste dal piano di studi, per gli immatricolati dall'A.A. 2021/2022 vengono riconosciute:

  • Lingua inglese: vengono riconosciuti 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 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.

Gli immatricolati fino all'A.A. 2020/2021 devono consultare le informazioni che si trovano qui.

Modalità di inserimento a librettorichiedere l’attestato o l'equipollenza al CLA e inviarlo alla Segreteria Studenti - Carriere per l’inserimento dell’esame in carriera, tramite mail: carriere.scienze@ateneo.univr.it

3. Competenze trasversali

Scopri i percorsi formativi promossi dal TALC - Teaching and learning center dell'Ateneo, destinati agli studenti regolarmente iscritti all'anno accademico di erogazione del corso https://talc.univr.it/it/competenze-trasversali

Modalità di inserimento a libretto: non è previsto l'inserimento dell'insegnamento nel piano di studi. Solo in seguito all'ottenimento dell'Open Badge verranno automaticamente convalidati i CFU a libretto. La registrazione dei CFU in carriera non è istantanea, ma ci saranno da attendere dei tempi tecnici.  

4. CONTAMINATION LAB

Il Contamination Lab Verona (CLab Verona) è un percorso esperienziale con moduli dedicati all'innovazione e alla cultura d'impresa che offre la possibilità di lavorare in team con studenti e studentesse di tutti i corsi di studio per risolvere sfide lanciate da aziende ed enti. Il percorso permette di ricevere 6 CFU in ambito D o F. Scopri le sfide: https://www.univr.it/clabverona

ATTENZIONE: Per essere ammessi a sostenere una qualsiasi attività didattica, incluse quelle a scelta, è necessario essere iscritti all'anno di corso in cui essa viene offerta. Si raccomanda, pertanto, ai laureandi delle sessioni di dicembre e aprile di NON svolgere attività extracurriculari del nuovo anno accademico, cui loro non risultano iscritti, essendo tali sessioni di laurea con validità riferita all'anno accademico precedente. Quindi, per attività svolte in un anno accademico cui non si è iscritti, non si potrà dar luogo a riconoscimento di CFU.

5. Periodo di stage/tirocinio

Oltre ai CFU previsti dal piano di studi (verificare attentamente quanto indicato sul Regolamento Didattico): qui informazioni su come attivare lo stage. 

Verificare nel regolamento quali attività possono essere di tipologia D e quali di tipologia F.

Insegnamenti e altre attività che si possono inserire autonomamente a libretto

Semester 1 From 10/2/23 To 1/26/24
years Modules TAF Teacher
1° 2° Introduction to smart contract programming for ethereum D Sara Migliorini (Coordinator)
Semester 2 From 3/4/24 To 6/14/24
years Modules TAF Teacher
1° 2° LaTeX Language D Enrico Gregorio (Coordinator)
1° 2° Python programming language D Carlo Combi (Coordinator)
1° 2° Programming Challanges D Romeo Rizzi (Coordinator)
1° 2° Protection of intangible assets (SW and invention)between industrial law and copyright D Mila Dalla Preda (Coordinator)
List of courses with unassigned period
years Modules TAF Teacher
1° 2° Cooperative Game Theory in the (Deep) RL Era D Alessandro Farinelli (Coordinator)

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: only in this way will you be able to receive notification of all the notices from your teachers and your secretariat via email and also via the Univr app.

Graduation

Deadlines and administrative fulfilments

For deadlines, administrative fulfilments and notices on graduation sessions, please refer to the Graduation Sessions - Science and Engineering service.

Need to activate a thesis internship

For thesis-related internships, it is not always necessary to activate an internship through the Internship Office. For further information, please consult the dedicated document, which can be found in the 'Documents' section of the Internships and work orientation - Science e Engineering service.

Final examination regulations

List of thesis 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 dell'analisi dei dati Various topics

Attendance modes and venues

As stated in the Teaching Regulations, attendance at the course of study is not mandatory.

Part-time enrolment is permitted. Find out more on the Part-time enrolment possibilities page.

The course's teaching activities take place in the Science and Engineering area, which consists of the buildings of Ca‘ Vignal 1, Ca’ Vignal 2, Ca' Vignal 3 and Piramide, located in the Borgo Roma campus. 
Lectures are held in the classrooms of Ca‘ Vignal 1, Ca’ Vignal 2 and Ca' Vignal 3, while practical exercises take place in the teaching laboratories dedicated to the various activities.

 


Career management


Student login and resources


Erasmus+ and other experiences abroad


Tutoring faculty members