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. 2017/2018

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 sem. Oct 2, 2017 Jan 31, 2018
II sem. Mar 1, 2018 Jun 15, 2018
Exam sessions
Session From To
Sessione invernale d'esame Feb 1, 2018 Feb 28, 2018
Sessione estiva d'esame Jun 18, 2018 Jul 31, 2018
Sessione autunnale d'esame Sep 3, 2018 Sep 28, 2018
Degree sessions
Session From To
Sessione Estiva Lauree Magistrali Jul 19, 2018 Jul 19, 2018
Sessione Autunnale Lauree Magistrali Oct 18, 2018 Oct 18, 2018
Sessione Invernale Lauree Magistrali Mar 21, 2019 Mar 21, 2019
Holidays
Period From To
Christmas break Dec 22, 2017 Jan 7, 2018
Easter break Mar 30, 2018 Apr 3, 2018
Patron Saint Day May 21, 2018 May 21, 2018
Vacanze estive Aug 6, 2018 Aug 19, 2018

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

Belussi Alberto

alberto.belussi@univr.it +39 045 802 7980
Foto,  February 9, 2017

Bloisi Domenico Daniele

domenico.bloisi@univr.it

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

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

Oliboni Barbara

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

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)
6
B
(ING-INF/05)
12
B
(ING-INF/05)
6
B
(ING-INF/05)
ModulesCreditsTAFSSD
6
B
(INF/01)
6
B
(ING-INF/05)
Other activitites
4
F
-
Final exam
24
E
-

1° Year

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

2° Year

ModulesCreditsTAFSSD
6
B
(INF/01)
6
B
(ING-INF/05)
Other activitites
4
F
-
Final exam
24
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°2 courses to be chosen 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

6

Scientific Disciplinary Sector (SSD)

ING-INF/05 - INFORMATION PROCESSING SYSTEMS

Language

Italian

Period

II sem. dal Mar 1, 2018 al Jun 15, 2018.

To show the organization of the course that includes this module, follow this link:  Course organization

Learning outcomes

The goal of this module is to introduce students to the main aspects of the computational complexity theory, and, in particular, to the NP-completeness theory and to the computational analysis of problems with respect to their approximability. Within the overall objectives of the CdS, this course allows students to widen and specialise their expertise in the analysis of algorithms and computational systems. It provides some advanced analysis tools to cope with non-trivial tasks.

The students will acquire skills and knowledge to understand and cope with the computational difficulty in solving some common task. Students will be able to independently analyze a new problem, understand its structure and what makes it difficult and propose possible alternative approach to its solution (approximation, parametrisation) in the absence of provably efficient solutions.

Program

Computational models, computational resources, efficient algorithms and tractable problems.

Relationships among computational problems. Polynomial reductions of one problem into another. The classes P, NP, co-NP. Notion of completeness. Proofs od NP-completeness: Cook's theorem; proofs of completeness using appropriate reductions. Search and Decision Problems. Self-Reducibility of NP-complete problems and existence of non-selfreducible problems. Recap of basic notions of computability: Turing Machines and Diagonalization. Hierarchy theorems for time complexity classes. Separability of classes and the existence of intermediate problem under the hypothesis the P is not equal NP.

Space Complexity. Models and fundamental difference between the use of time resource and the space resource. The space complexity classes NL and L and their relationship with the time complexity class P. The centrality of the reachability problem for the study of space complexity. Completeness for space complexity classes: Log-space reductions; NL-completeness of reachability. Non-determinism and space complexity. Savitch theorem and Immelmann-Szelepcsenyi theorem. The classes PSPACE and NPSPACE. Examples of reductions to prove PSPACE-completeness.

Introduction to the approximation algorithms for optimisation problems. Examples of approximation algorithms. Classification of problems with respect to their approximabuility. The classes APX, PTAS, FPTAS. Notion of inapproximability; the gap technique to prove inapproximability results; examples of approximation preserving reductions. Examples of simple randomised algorithms in solving hard problems.


Recommended Prerequisites
-------------------------
To attend the course in a productive way, a student should be confident with the following topics:
1. Basic data structures as list, stack, queue, tree, heap.
2. Graph representation and fundamental graph algorithms:
2.1 Graph visit: BFS, DFS.
2.2 Topological ordering. Connected component.
2.3 Minimal spanning tree. Kruskal and Prim algorithm.
2.4 Single-source shortest path: Dijkstra algorithm and Bellman-Ford one.
2.5 All-pairs shortest path: Floyd-Warshall algorithm and Johnson one.
2.6 Max flow: Ford-Fulkerson algorithm.

A recommended book to revise the above topics is ``Introduction to Algorithms" di T. H. Cormen, C. E. Leiserson, R. L. Rivest e C. Stein (3 ed.).

Bibliografia

Reference texts
Author Title Publishing house Year ISBN Notes
J. Kleinberg, É. Tardos Algorithm Design (Edizione 1) Addison Wesley 2006 978-0321295354
Ingo Wegener Complexity Theory Springer 2005
Christos H. Papadimitriou Computational complexity Addison Wesley 1994 0201530821
S. Arora, B. Barak Computational Complexity. A modern approach (Edizione 1) Cambridge University Press 2009 9780521424264
Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein Introduction to Algorithms (Edizione 3) MIT Press 2009 978-0-262-53305-8
Michael Sipser Introduction to the Theory of Computation PWS 1997 053494728X
Cristopher Moore, Stephan Mertens The Nature of Computation Oxford 2011

Examination Methods

The exam verifies that the students have acquired sufficient understanding of the basic complexity classes and the necessary skills to analyse and classify a computational problem.

The exam consists of a written test with open questions. The test includes some mandatory exercises and a set of exercises among which the student can choose what to work on. The mandatory exercises are meant to evaluate the ability of the student to apply knowledge: reproducing (simple variants of) theoretical results and algorithms seen in class for classical problems. "Free-choice" exercises test the analytical skills acquired by the students to model "new" toy problems and analyse its computational complexity using reductions.

The grade for the module "complexity" is averaged (50%) with the grade for the module algorithm to determine the final grade.

Type D and Type F activities

Modules not yet included

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
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

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.