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. 2019/2020

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, 2019 Jan 31, 2020
II semestre Mar 2, 2020 Jun 12, 2020
Exam sessions
Session From To
Sessione invernale d'esame Feb 3, 2020 Feb 28, 2020
Sessione estiva d'esame Jun 15, 2020 Jul 31, 2020
Sessione autunnale d'esame Sep 1, 2020 Sep 30, 2020
Degree sessions
Session From To
Sessione Estiva. Jul 16, 2020 Jul 16, 2020
Sessione Autunnale. Oct 15, 2020 Oct 15, 2020
Sessione Invernale. Mar 18, 2021 Mar 18, 2021
Holidays
Period From To
Festa di Ognissanti Nov 1, 2019 Nov 1, 2019
Festa dell'Immacolata Dec 8, 2019 Dec 8, 2019
Vacanze di Natale Dec 23, 2019 Jan 6, 2020
Vacanze di Pasqua Apr 10, 2020 Apr 14, 2020
Festa della Liberazione Apr 25, 2020 Apr 25, 2020
Festa del lavoro May 1, 2020 May 1, 2020
Festa del Santo Patrono May 21, 2020 May 21, 2020
Festa della Repubblica Jun 2, 2020 Jun 2, 2020
Vacanze estive Aug 10, 2020 Aug 23, 2020

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 M O P Q R S V

Baruffi Maria Caterina

mariacaterina.baruffi@univr.it +39 045 8028827 - 47

Belussi Alberto

alberto.belussi@univr.it +39 045 802 7980

Bombieri Nicola

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

Bonacina Maria Paola

mariapaola.bonacina@univr.it +39 045 802 7046

Boscaini Maurizio

maurizio.boscaini@univr.it

Busato Federico

federico.busato@univr.it

Calanca Andrea

andrea.calanca@univr.it +39 045 802 7847

Carra Damiano

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

Castellani Umberto

umberto.castellani@univr.it +39 045 802 7988

Cicalese Ferdinando

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

Cristani Matteo

matteo.cristani@univr.it 045 802 7983

Cristani Marco

marco.cristani@univr.it +39 045 802 7841

Cubico Serena

serena.cubico@univr.it 045 802 8132

Dall'Alba Diego

diego.dallalba@univr.it +39 045 802 7074

Dalla Preda Mila

mila.dallapreda@univr.it

Farinelli Alessandro

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

Favretto Giuseppe

giuseppe.favretto@univr.it +39 045 802 8749 - 8748

Fiorini Paolo

paolo.fiorini@univr.it 045 802 7963

Franco Giuditta

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

Fummi Franco

franco.fummi@univr.it 045 802 7994

Giachetti Andrea

andrea.giachetti@univr.it +39 045 8027998

Giacobazzi Roberto

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

Maris Bogdan Mihai

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

Masini Andrea

andrea.masini@univr.it 045 802 7922

Mastroeni Isabella

isabella.mastroeni@univr.it +39 045 802 7089

Menegaz Gloria

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

Merro Massimo

massimo.merro@univr.it 045 802 7992

Muradore Riccardo

riccardo.muradore@univr.it +39 045 802 7835

Murino Vittorio

vittorio.murino@univr.it 045 802 7996

Oliboni Barbara

barbara.oliboni@univr.it +39 045 802 7077

Paci Federica Maria Francesca

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

Pravadelli Graziano

graziano.pravadelli@univr.it +39 045 802 7081

Quaglia Davide

davide.quaglia@univr.it +39 045 802 7811

Rizzi Romeo

romeo.rizzi@univr.it +39 045 8027088

Romeo Alessandro

alessandro.romeo@univr.it +39 045 802 7974-7936; Lab: +39 045 802 7808

Segala Roberto

roberto.segala@univr.it 045 802 7997

Villa Tiziano

tiziano.villa@univr.it +39 045 802 7034

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.

CURRICULUM TIPO:
ModulesCreditsTAFSSD
12
B
(ING-INF/05)
12
B
(ING-INF/05)
6
B
(ING-INF/05)
6
B
(ING-INF/05)
ModulesCreditsTAFSSD
6
B
(ING-INF/05)
6
B
(INF/01)
Other activities
4
F
-
Final exam
24
E
-

1° Year

ModulesCreditsTAFSSD
12
B
(ING-INF/05)
12
B
(ING-INF/05)
6
B
(ING-INF/05)
6
B
(ING-INF/05)

2° Year

ModulesCreditsTAFSSD
6
B
(ING-INF/05)
6
B
(INF/01)
Other activities
4
F
-
Final exam
24
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°2 modules among the following
6
C
(INF/01)
6
C
(INF/01)
6
C
(SECS-P/10)
6
C
(INF/01)
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

4S02709

Credits

12

Coordinatore

Romeo Rizzi

The teaching is organized as follows:

Complessità

Credits

6

Period

II semestre

Academic staff

Ferdinando Cicalese

Algoritmi

Credits

6

Period

II semestre

Academic staff

Romeo Rizzi

Learning outcomes

We refer to the web pages of the modules for the complete and precise list of goals of the course as divided into its two parts. We limit ourselves to focus here on the overall targets of the Algorithm course, as a whole, which is to expose some aspects of the deep and important dialectic exchange between the search for algorithmic solutions and the study of the complexity of problems. Here, we can only touch upon the nature of the privileged relationship of these two disciplines, and their actual unity (like the Yin and Yang sides of a single one art), hoping, nonetheless, that this will help orienting the student in tackling this adventurous path with the right enthusiasm and perspective. Algorithms are the backbone and the substance of information technologies, but at the same time their study goes beyond the "mere" computer science and is pervasive to all the disciplines that are problem-bearers. The design of an algorithm starts from the study of the structure of the problem to be solved and it usually represents the highest achievement of this process. The study of algorithms requires and offers methodologies and techniques of problem solving, logical and mathematical skills. The course therefore aims to provide students with fundamental skills and methodologies for the analysis of problems and the design of the algorithms for solving them. Particular emphasis is given to the efficiency of the algorithms themselves, and the theory of computational complexity plays a profound methodological role in the analysis of problems. For non-trivial problems, the process of algorithm design rests on the theory of complexity not only to identify on which questions, and subproblems, it may make sense to concentrate efforts, but also as a dialectical counterpart providing the right language to disclose the subtle nuances of the problem and guiding towards the appropriate way of addressing its solution. A goal of the course is to highlight and illustrate the symbiosis between the competences (algorithm design and the study of problem complexity) which are addressed in the two modules. With reference to the overall didactic aims of the Master program, the course leads students to deepen and expand the three-year training in the field of analysis and evaluation of problems, algorithms, and computational models, providing a wealth of advanced tools to address non-trivial problems in different IT fields.

Program

1. The workflow of problem solving: analysis and comprehension of the problem, conception of an algorithmic solution, design of an efficient algorithm, planning the implementation, conducting the implementation, testing and debugging.

2. Methodology in analyzing a problem:
The study of special cases. Particularization and generalization.
Building a dialog with the problem. Conjectures. Simplicity assumptions.
Solving a problem by reducing it to another. Reductions among problems to organize them into classes. Reducing problems to isolate the most fundamental questions. The role of complexity theory in classifying problems into classes. The role of complexity theory in analyzing problems. Counterexamples and NP-hardness proofs. Good conjectures and good characterizations. The belief can make conjectures true. Decomposing problems and inductive thinking.

3. Algorithm design general techniques.
Recursion. Divide et impera. Recursion with memoization. Dynamic programming (DP). Greedy.
DP on sequences. DP on DAGs. More in depth: good characterization of DAGs and scheduling, composing partial orders into new ones.
DP on trees. More in depth: adoption of the children one by one; advantages of an edge-centric vision over the node-centric one.
The asymptotic eye on worst case performance guides the design of algorithms:
the binary search example; negligible improvements; amortized analysis.
Some data structures: binary heaps; prefix-sums; Fenwick trees; range trees.

4. Algorithms on graphs and digraphs.
Bipartite graphs: recognition algorithms and good characterizations.
Eulerian graphs: recognition algorithms and good characterizations.
Shortest paths. Minimum spanning trees.
Maximum flows and minimum cuts.
Bipartite matchings and node covers.
The kernel of a DAG. Progressively finite games. Sums of games.

5. General hints on implementing, coding, testing and debugging.
Plan your implementation. Anticipate the important decisions, and realize where the obscure points are. Try to go round the most painful issues you foresee. Code step by step. Verify step by step. Use the assert. Testing and debugging techniques. Self-certifying algorithms.

Examination Methods

Because of the CoVid19 emergency the organization and procedures of the exam have departed from what written more below in the official version. Since things are in continuous evolution, and we want to make sure no student gets lost, we redirect the student directly to the reference service site that we can maintain constantly updated:

http://profs.sci.univr.it/~rrizzi/classes/Algoritmi/index.html

We warmly advise every student to subscribe to the Telegram group for the 2020 edition of the course. This and other useful resources are conveniently accessed from the URL here above.


HERE BELOW FOLLOWS THE OFFICIAL VERSIONE THAT WAS INSERTED HERE AT THE BEGINNING OF THE ACADEMIC YEAR:

Students must face a 5 hours test held in computer room. Here, they are assigned some problems. The students must analyze and comprehend the problem and its structure, think of possible algorithmic solutions, design an algorithm and implement it in c/c++ or Pascal. The most efficient their algorithm is, the more points they will get.
During the exam, the students can submit their source codes to a site organized precisely as the one they have experimented during the exercise sessions in lab and/or at home. In this way they can get an immediate and contextual feedback that can guide them in conducting and managing their exam at their best.
Their solutions are evaluated based on the subtasks of the problem that they can solve correctly within the allotted computation time and memory as fixed by each single problem or subtask of it.
In this way, the efficiency of the solutions and algorithms they have designed and coded determines the final scores.
It is guaranteed that, at every exam session, at least one problem will be chosen among the problems that have been proposed and made available at the site for the home/laboratory exercises.
Often, other problems are taken from the COCI competitions or among problems of the olympiads in informatics or in problem solving.
Even after having achieved a positive vote, the student can participate to later exam sessions and see whether he can improve its current mark without any risk of reducing or tampering it anyhow: our policy is to keep the best mark ever.
To the mark from the exam are summed the points the students may have possibly collected in projects. As possible projects, this year we proposed the students to help us in designing problems and/or help us in the realization of a new system for the compilation of problem packages of new conception.
As such, each student has its own mark wallet. When the students decides he wants his (positive) mark registered, then the final mark is obtained as the average with the mark for the Computational Complexity twin module (which must also be positive in order to proceed) and this average is the mark for the whole course.

The site for the home/laboratory exercises:
https://cms.di.unipi.it/algo/

and the problems you find there, are your first resource for preparing to your exam. The system you will encounter at the exam is a clone of it.

For more information on the modalities and possibilities at the exam, and for further preparation material, explore the site of the course (and help us improving it):

http://profs.sci.univr.it/~rrizzi/classes/Algoritmi/index.html

Here you can find the wallet of your marks for both the "Algorithms" and the "Computationl Complexity" modules comprising the course (if any), plus your extra scores for Algorithms in case you have collected any of them with projects.
You will also find here the problems given at previous exam sessions,
and more detailed instructions on the procedures for the exam and for the composition and registration of your final mark.

Bibliografia

Reference texts
Author Title Publishing house Year ISBN Notes
J. Kleinberg, É. Tardos Algorithm Design (Edizione 1) Addison Wesley 2006 978-0321295354
Garey, M. R. and Johnson, D. S. Computers intractability: a guide to the theory of NP-completeness Freeman 1979 0-7167-1045-5
T. Cormen, C. Leiserson, R. Rivest, C. Stein Introduzione agli Algoritmi e Strutture Dati (Edizione 2) McGraw-Hill 2005 88-386-6251-7
Michael Sipser Introduction to the Theory of Computation PWS 1997 053494728X
Cristopher Moore, Stephan Mertens The Nature of Computation Oxford 2011
Sanjoy Dasgupta, Christos Papadimitriou, Umesh Vazirani Algorithms (Edizione 1) McGraw-Hill Higher Education 2007 978-0-07-352340-8
Steven S Skiena, Miguel A. Revilla Programming Challenges: The Programming Contest Training Manual (Edizione 2013) Springer New York, 2013 2003 147578970X

Type D and Type F activities

1° periodo di lezioni From 9/30/19 To 12/14/19
years Modules TAF Teacher
1° 2° The fashion lab (1 ECTS) D Not yet assigned
I semestre From 10/1/19 To 1/31/20
years Modules TAF Teacher
1° 2° Python programming language D Maurizio Boscaini (Coordinatore)
II semestre From 3/2/20 To 6/12/20
years Modules TAF Teacher
1° 2° CyberPhysical Laboratory D Andrea Calanca (Coordinatore)
1° 2° C++ Programming Language D Federico Busato (Coordinatore)
1° 2° Matlab-Simulink programming D Bogdan Mihai Maris (Coordinatore)
List of courses with unassigned period
years Modules TAF Teacher
1° 2° Corso Europrogettazione D Not yet assigned
1° 2° The course provides an introduction to blockchain technology. It focuses on the technology behind Bitcoin, Ethereum, Tendermint and Hotmoka. D Matteo Cristani

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.

Graduation

List of theses and work experience 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 delle basi di dati/sistemi informativi Various topics

Gestione carriere


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.