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 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

symbol email simone.accordini@univr.it symbol phone-number +39 045 8027657

Bicego Manuele

symbol email manuele.bicego@univr.it symbol phone-number +39 045 802 7072

Bombieri Cristina

symbol email cristina.bombieri@univr.it symbol phone-number 045-8027284

Bombieri Nicola

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

Combi Carlo

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

Constantin Gabriela

symbol email gabriela.constantin@univr.it symbol phone-number 045-8027102

Daducci Alessandro

symbol email alessandro.daducci@univr.it symbol phone-number +39 045 8027025

Delledonne Massimo

symbol email massimo.delledonne@univr.it symbol phone-number 045 802 7962; Lab: 045 802 7058

Fiorini Paolo

symbol email paolo.fiorini@univr.it symbol phone-number 045 802 7963

Fratea Caterina

symbol email caterina.fratea@univr.it symbol phone-number 045 842 5358

Fummi Franco

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

Giacobazzi Roberto

symbol email roberto.giacobazzi@univr.it symbol phone-number +39 045 802 7995

Giugno Rosalba

symbol email rosalba.giugno@univr.it symbol phone-number 0458027066

Laudanna Carlo

symbol email carlo.laudanna@univr.it symbol phone-number 045-8027689

Liptak Zsuzsanna

symbol email zsuzsanna.liptak@univr.it symbol phone-number +39 045 802 7032

Malerba Giovanni

symbol email giovanni.malerba@univr.it symbol phone-number 045/8027685

Marcon Alessandro

symbol email alessandro.marcon@univr.it symbol phone-number +39 045 802 7668

Maris Bogdan Mihai

symbol email bogdan.maris@univr.it symbol phone-number +39 045 802 7074

Marzola Pasquina

symbol email pasquina.marzola@univr.it symbol phone-number 045 802 7816 (ufficio); 045 802 7614 (laboratorio)

Mazzi Giulio

symbol email giulio.mazzi@univr.it

Menegaz Gloria

symbol email gloria.menegaz@univr.it symbol phone-number +39 045 802 7024

Perduca Massimiliano

symbol email massimiliano.perduca@univr.it symbol phone-number +39 045 802 7984

Sala Pietro

symbol email pietro.sala@univr.it symbol phone-number 0458027850

Salvagno Gian Luca

symbol email gianluca.salvagno@univr.it symbol phone-number 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.

activated in the A.Y. 2021/2022
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.




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

Teaching code

4S004553

Teacher

Pietro Sala

Coordinatore

Pietro Sala

Credits

6

Language

English en

Scientific Disciplinary Sector (SSD)

INF/01 - INFORMATICS

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.

Bibliography

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.

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

Upon completion of the Master’s degree dissertation, students are awarded 24 CFU, which equates to no more than 4-5 months of full-time work. The dissertation may be written and presented in English or Italian, also using multimedia tools such as presentations and videos.

Goals
The primary goal of a dissertation is to develop an original study that may include an application project or a theoretical topic related to specific design issues, or a critical review of the most recent developments in a given field of study. During the preparation of the dissertation, under the guidance of the Supervisor and co-supervisors (if any), the student is expected to conduct an in-depth study of the chosen topic, while gaining the ability to summarise and creatively apply the knowledge acquired. The dissertation should focus on topics of bioinformatics and medical informatics, or closely related areas of study. The work shall consist in the written presentation of activities that may be structured as follows:

  • design and development of applications or systems;
  • critical analysis of contributions from the scientific literature;
  • original research contributions.

The dissertation may be written either in English or in Italian, and can be presented either in English or in Italian, also relying on multimedia tools such as presentations and videos. Should the dissertation be written in Italian, the work will need to include an abstract in English.

Assessment methods and examination procedures
The final examination consists in writing a Master’s degree dissertation, which will engage the student in a work of research, formalisation, design or development, thus contributing to complete their technical and scientific training. Each dissertation can be either internal or external, depending on whether it is carried out at the University of Verona or in collaboration with another institution. For each dissertation a Supervisor, one or more co-supervisors (optional) and an Examiner will be appointed. The Examiner is appointed by the Computer Science Teaching Committee at least 20 days before the presentation of the dissertation, once the student's eligibility to take the Master's degree examination has been verified. With regard to the legal aspects related to the dissertation and its scientific outcomes (e.g. intellectual property of research outcomes), please refer to the relevant legislation and the University Regulations.

Evaluation of the dissertation
The Supervisor, the co-supervisor/s (if any) and the Examiner will evaluate the dissertation based on the following criteria:

  1. level of in-depth analysis carried out, in relation to the most recent developments in the areas related to information technology, with a focus on medical and biological applications;
  2. scientific and/or technological outcomes of the dissertation;
  3. student’s critical thinking;
  4. student’s experimental and/or formal development;
  5. student’s ability to carry out independent work (this point will not be assessed by the Examiner);
  6. value of the methodologies used;
  7. accuracy in planning and writing the dissertation.

Graduation mark
The graduation mark (based on a 110-point scale) is a whole value between 66/110 and 110/110 and is calculated by adding together the following elements (then rounding the result to the nearest whole number, e.g. 93.50 => 94; 86.49 => 86):

  • 1) the average of the marks gained in the modules, weighted according to CFU, converted to a 110-point scale;
  • 2) evaluation of the dissertation and the oral presentation during the final examination, based on the following methods:
    • a) each of the points 1-7 listed above will be assigned a coefficient between 0 and 1 (fractional coefficient with one decimal place);
    • b) the quality of the presentation will be assessed by awarding a coefficient between 0 and 1 (fractional coefficient with one decimal place);
    • c) the sum of the points resulting from (a) and (b).

The Graduation Committee may award one extra point in the following cases: cum laude honours obtained in the exams taken during the degree programme; participation in internships officially recognised by the Computer Science Teaching Committee; taking extra modules; and the achievement of the degree in a time that is shorter than the normal duration of the degree programme. If the final score is 110/110, the Graduation Committee may award cum laude honours by unanimous decision.

External dissertations
An external dissertation is a work carried out in collaboration with an institution/body other than the University of Verona. In this case, the topic of the dissertation must be agreed in advance with a Supervisor from the University of Verona. In addition, the student must indicate at least one co- supervisor belonging to the external institution/body, who will support the student during the work on the dissertation. The Supervisor and the co- supervisors must be indicated in the online graduation application. The insurance aspects relating to the student's stay at the external institution are regulated by the regulations in force at the University of Verona. If the dissertation involves a period of training at the external institution/body, then it is necessary that the University of Verona enters into a specific agreement with such institution/body. The scientific outcomes of the dissertation will be available to all parties involved. In particular, the contents and results of the dissertation are to be considered public. For all matters not strictly scientific (e.g. agreements, insurance) the resolution of the Academic Senate of 12 January 1999 shall be taken as a reference.

Supervisor, co-supervisors, examiners
The dissertation presentation is introduced by the Supervisor. Professors belonging to the Master’s degree programme in Medical Bioinformatics, the Department of Computer Science, and any associated departments may be appointed as Supervisors, as well as any lecturers from the University of Verona whose area of interest is included in the Scientific-disciplinary Sectors (SSD) ING/INF/05 and INF/01. In addition to those who have the above requirements to be appointed as Supervisor, the following individuals may be appointed as co-supervisors: researchers working in external research institutes, research grant holders, post-doctoral fellowship holders, PhD students, technical staff of the Department, external experts appointed by an Italian University, corporate officers who have a remarkable experience in the field relevant to the topic of the dissertation. Examiners may be appointed among professors of the University of Verona, working in the Scientific- disciplinary Sectors (SSD) included in the educational offer of the Master’s degree programme in Medical Bioinformatics, and experts in the specific field of the dissertation topic.

Procedures and deadlines
The student who is about to complete their studies must identify a dissertation topic, proposed or approved by a Supervisor or co-supervisor/s (if any). When the work is nearing completion, the student must submit to the Teaching and Student Services Unit the graduation application, which must contain the title of the dissertation (even provisional), the name of the Supervisor, co-supervisor/s (only for external dissertations) and Examiner. Subsequently, on dates established by the Teaching and Student Services Unit, and in any case no later than 20 days before the graduation, the student must submit the graduation application form with the final title of the dissertation, which must be signed by the Supervisor. These documents must be delivered in accordance with the terms established by the Teaching and Student Services Unit.

The student will need to:

  • i) upload a copy of their dissertation on ESSE3;
  • ii) send a copy of their dissertation in PDF format to their Examiner.

In order to be admitted to the final examination, the student must have acquired the CFU in the SSD (Scientific-Disciplinary Sectors) set out in the Master’s degree regulations and teaching plan, and be up to date with the payment of their tuition fees. The Teaching and Student Services Unit of the Master's degree programme will invite all the Supervisors and co-supervisors involved, providing them with information about the date and time of the final examination.

Graduation Committee
The Graduation Committee shall include five members, of which at least four are professors in the Master's degree programme in Medical Bioinformatics. Based on the number of graduates, the Computer Science Teaching Committee will identify the most appropriate organisational methods for administering the examination, and it shall make available the calendar of tests at least one week before the examination itself. The procedures and deadlines for the submission of the graduation application are established by the Computer Science Teaching Committee and by the relevant offices.


Attendance

As stated in the Teaching Regulations for the A.Y. 2022/2023, 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


Student login and resources