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
Holidays
Period From To
Festa dell'Immacolata Dec 8, 2020 Dec 8, 2020
Vacanze Natalizie Dec 24, 2020 Jan 3, 2021
Vacanze Pasquali Apr 2, 2021 Apr 5, 2021
Festa del Santo Patrono May 21, 2021 May 21, 2021
Festa della Repubblica Jun 2, 2021 Jun 2, 2021
Vacanze estive Aug 9, 2021 Aug 15, 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

B C D F G H I P Q S Z

Badino Massimiliano

massimiliano.badino@univr.it +39 045 802 8459

Bazzani Claudia

claudia.bazzani@univr.it 0458028734
LBO,  January 31, 2017

Bullini Orlandi Ludovico

ludovico.bulliniorlandi@univr.it 045 802 8095

Carra Damiano

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

Carradore Marco

marco.carradore@univr.it

Castellini Alberto

alberto.castellini@univr.it +39 045 802 7908

Ceccato Mariano

mariano.ceccato@univr.it

Chiarini Andrea

andrea.chiarini@univr.it 045 802 8223

Cordoni Francesco Giuseppe

francescogiuseppe.cordoni@univr.it

Dai Pra Paolo

paolo.daipra@univr.it +39 0458027093

Dalla Preda Mila

mila.dallapreda@univr.it

Di Persio Luca

luca.dipersio@univr.it +39 045 802 7968

Farinelli Alessandro

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

Giachetti Andrea

andrea.giachetti@univr.it +39 045 8027998

Paci Federica Maria Francesca

federicamariafrancesca.paci@univr.it +39 045 802 7909

Quintarelli Elisa

elisa.quintarelli@univr.it +39 045 802 7852

Spoto Nicola Fausto

fausto.spoto@univr.it +39 045 8027940

Zardini Alessandro

alessandro.zardini@univr.it 045 802 8565

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
ModulesCreditsTAFSSD
9
B/C
(IUS/01 ,M-FIL/03)
Training
6
F
-
Final exam
22
E
-

1° Year

ModulesCreditsTAFSSD

2° Year

ModulesCreditsTAFSSD
9
B/C
(IUS/01 ,M-FIL/03)
Training
6
F
-
Final exam
22
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°1 module among the following (1st year: Big Data epistemology and Social research; 2nd year: Cybercrime, Data protection in business organizations, Comparative and Transnational Law & Technology)
6
C
(IUS/17)
6
C
(SPS/07)
Between the years: 1°- 2°2 courses among the following (1st year: Business analytics, Digital Marketing and market research; 2nd year: Logistics, Operations & Supply Chain, Digital transformation and IT change, Statistical methods for Business intelligence)
Between the years: 1°- 2°2 courses among the following (1st year: Complex systems and social physics, Discrete Optimization and Decision Making, 2nd year: Statistical models for Data Science, Continuous Optimization for Data Science, Network science and econophysics, Marketing research for agrifood and natural resources)
Between the years: 1°- 2°2 courses among the following (1st year: Data Visualisation, Data Security & Privacy, Statistical learning, Mining Massive Dataset, 2nd year: Machine Learning for Data Science)
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

4S009068

Coordinatore

Damiano Carra

Credits

6

Scientific Disciplinary Sector (SSD)

ING-INF/05 - INFORMATION PROCESSING SYSTEMS

Language

English en

Period

II semestre dal Mar 1, 2021 al Jun 11, 2021.

Learning outcomes

The course aims to present the main algorithmic solutions for the analysis and extraction of information from large amounts of data. Particular emphasis is given to distributed approaches and parallel algorithms.

At the end of the course the student has to show to have acquired the following skills:
● the knowledge necessary for the design of algorithms for the analysis of unstructured data and the interpretation of the results
● ability to develop cost/benefit analysis of the developed data analysis models
● ability to compare different data analysis techniques, choosing the most suitable among them according to the available computing resources and to design innovative solutions appropriately
● acquisition of the basis for continuing your studies independently in the context of developing advanced analyzes of large amounts of data.

Program

- Data Mining introduction
- Finding Similar Items
- Mining Data Streams
- Frequent Itemsets
- Clustering
- Recommendation Systems
- Mining Social-Network Graphs
- Large-Scale Machine Learning

Bibliografia

Reference texts
Author Title Publishing house Year ISBN Notes
Jure Leskovec, Anand Rajaraman, Jeff Ullman Mining of Massive Datasets (Edizione 3) Cambridge University Press 2020 9781108476348 Book freely available at http://www.mmds.org/

Examination Methods

Examination consists of a project and the corresponding documentation. The project aims at verifying the comprehension of course contents and the capability to apply these contents in the resolution of a problem. The project topic is agreed with the teacher and focus on specific case studies. The project includes the performance evaluation for different input sizes, and the evaluation of the implementation alternatives. After the evaluation of the project documentation, the student may give an oral exam where the details of the project are discussed.

Type D and Type F activities

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.

Gestione carriere


Graduation

List of theses and work experience proposals

theses proposals Research area
Domain Adaptation Computer Science and Informatics: Informatics and information systems, computer science, scientific computing, intelligent systems - Computer graphics, computer vision, multi media, computer games
Domain Adaptation Computer Science and Informatics: Informatics and information systems, computer science, scientific computing, intelligent systems - Machine learning, statistical data processing and applications using signal processing (e.g. speech, image, video)
Domain Adaptation Computing Methodologies - IMAGE PROCESSING AND COMPUTER VISION
Domain Adaptation Computing methodologies - Machine learning

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.

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.