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. 2009/2010

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
1st Semester Oct 1, 2009 Jan 31, 2010
2nd Semester Mar 1, 2010 Jun 15, 2010
Exam sessions
Session From To
Sessione straordinaria Feb 1, 2010 Feb 28, 2010
Sessione estiva Jun 16, 2010 Jul 31, 2010
Sessione autunnale Sep 1, 2010 Sep 30, 2010
Degree sessions
Session From To
Sessione autunnale Sep 29, 2009 Sep 29, 2009
Sessione straordinaria Dec 10, 2009 Dec 10, 2009
Sessione invernale Mar 17, 2010 Mar 17, 2010
Sessione estiva Jul 20, 2010 Jul 20, 2010
Holidays
Period From To
Festa di Ognissanti Nov 1, 2009 Nov 1, 2009
Festa dell'Immacolata Concezione Dec 8, 2009 Dec 8, 2009
Vacanze Natalizie Dec 21, 2009 Jan 6, 2010
Vacanze Pasquali Apr 2, 2010 Apr 6, 2010
Festa della Liberazione Apr 25, 2010 Apr 25, 2010
Festa del Lavoro May 1, 2010 May 1, 2010
Festa del Santo Patrono di Verona S. Zeno May 21, 2010 May 21, 2010
Festa della Repubblica Jun 2, 2010 Jun 2, 2010
Vacanze Estive Aug 9, 2010 Aug 15, 2010

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 S V

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

Carra Damiano

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

Castellani Umberto

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

Combi Carlo

carlo.combi@univr.it 045 802 7985

Cristani Matteo

matteo.cristani@univr.it 045 802 7983

Cristani Marco

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

Di Pierro Alessandra

alessandra.dipierro@univr.it +39 045 802 7971

Farinelli Alessandro

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

Favretto Giuseppe

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

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

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

Monti Francesca

francesca.monti@univr.it 045 802 7910

Muradore Riccardo

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

Oliboni Barbara

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

Posenato Roberto

roberto.posenato@univr.it +39 045 802 7967

Pravadelli Graziano

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

Quaglia Davide

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

Segala Roberto

roberto.segala@univr.it 045 802 7997

Vigano' Luca

luca.vigano@univr.it

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)
6
B
(ING-INF/05)
12
B
(ING-INF/05)
6
B
(ING-INF/05)
ModulesCreditsTAFSSD
6
B
(INF/01)
Altre attivita' formative
4
F
-

1° Year

ModulesCreditsTAFSSD
12
B
(ING-INF/05)
6
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)
Altre attivita' formative
4
F
-
Modules Credits TAF SSD
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

Roberto Posenato

The teaching is organized as follows:

Algoritmi avanzati

Credits

4

Period

1st Semester

Academic staff

Roberto Posenato

Complessità

Credits

4

Period

1st Semester

Academic staff

Roberto Posenato

Intelligenza artificiale

Credits

4

Period

1st Semester

Academic staff

Maria Paola Bonacina

Learning outcomes

Module: ALGORITMI AVANZATI
-------
The goal of this course is to introduce some advanced paradigms for algorithms development and analysis in order to determine good approximate solutions for hard optimization problems.

Module: COMPLESSITÀ
-------
The goal of this module is to introduce students to computational complexity theory in general, to the NP-completeness theory in detail and to computational analysis of problems with respect to their approximability.

Module: INTELLIGENZA ARTIFICIALE
-------
The class presents the main techniques for problem solving, based on the central paradigm of symbolic representation. The objective is to provide the students with the ability to design, apply and evaluate algorithms for difficult problems, meaning that their mechanical solution captures aspects of artificial intelligence or computational rationality.

Program

Module: ALGORITMI AVANZATI
-------
Main concepts recall about computational problems: definition, instances, encoding, precise and approximate models. Optimization computational problem.
Main concepts recall about algorithms: computational resources, input encoding, input size/cost, computational time. Worst and average analysis. Computational time and growth order.
Computational time vs. hardware improvements: main relations. Efficient algorithms and tractable problems.

Divide et impera paradigm
Definition and application to some problems.

Greedy paradigm
Definition and application to some problems. Matroids and greedy algorithms.

Backtracking technique
Definition and application to some problems.

Branch & Bound technique
Definition and application to some problems.

Dynamic programming paradigm
Definition and application to some problems.
Memoization and Dynamic programming.

Local search technique
Definition and application to some problems.

Approximations algorithms
Definition and some application examples.
Simulated annealing.
Tabu search.

Probabilistic algorithms
Definition and few application examples.
Numerical probabilistic algorithms, Monte Carlo algorithms and Las Vegas algorithms. Examples: Buffon's needle, Pattern Matching and Universal hashing.


Module: COMPLESSITÀ
-------
Introduction.
Computational model concept, computational resource, efficient algorithm and tractable problem.

Computational models
Turing Machine (MdT). MdT extension: multi-tape MdT (k-MdT). MdT and languages: the difference between accepting and deciding a language.
Random Access Machine (RAM) computational model. Computation time considering uniform cost criterion or logarithmic cost one.

Time Complexity
Computational class TIME(). Theorem about polynomial relation between k-MdT computations and MdT ones (sketch of proof).
Theorem about simulation cost of a MdT by a RAM.
Theorem about simulation cost of a RAM program by a MdT.
Sequential Computation Thesis and its consequences.
Linear Speed-up Theorem and its consequences.
P Computational Class.
Problems in P: PATH, MAX FLOW, PERFECT MATCHING.
Extension of MdT: non-deterministic MdT (NMdT).
Time resource for k-NMdT. NTIME() computational class. Relation between NMdT and MdT.
NP Computational Class.
An alternative characterization of NP: polynomial verifiers.
EXP Computation Class.

Space Complexity.
Space complexity concept. MdT with I/O. Computational Classes: SPACE() and NSPACE().
Compression Theorem.
Computational Classes: L and NL.
Example of problems: PALINDROME ∈ L and PATH ∈ NL.
Theorems about relations between space and time for a MdT with I/O. Relations between complexity classes.
Proper function concept and example of proper functions.
Borodin Gap Theorem.
Reachability method.
Theorem about space-time classes: NTIME(f(n)) ⊆ SPACE(f(n)), NSPACE(f(n)) ⊆ TIME(k(log n+f(n))).
Universal MdT. The Hf set. Lemma 1 and 2 for time hierarchy theorem.
Time Hierarchy Theorem: strict and no-strict versions. P ⊂ EXP Corollary.
Space Hierarchy Theorem. L ⊂ PSPACE Corollary. Savitch Theorem. SPACE(f(n))=SPACE(f(n)^2) corollary. PSPACE=NPSPACE Corollary.

Reductions and completeness.
Reduction concept and logarithmic space reduction.
HAMILTON PATH ≤log SAT, PATH ≤log CIRCUIT VALUE, CIRCUIT SAT ≤log SAT.
Language completeness concept.
Closure concept with respect to reduction. Class reduction of L, NL, P, NP, PSPACE and EXP.
Computation Table concept.
Theorem about P-completeness of CIRCUIT VALUE problem.
Cook Theorem: an alternative proof.
Gadget concept and completeness proof of: INDEPENDENT SET, CLIQUE, VERTEX COVER and others.


Module: INTELLIGENZA ARTIFICIALE
-------
Problem solving as search in a state space; un-informed search procedures; informed search procedures and heuristic search. Constraint problem solving. Knowledge representation: use of propositional logic and first-order logic; normal forms; equality. Algorithms for satisfiability (SAT). Theorem proving: resolution and rewriting.

Examination Methods

Module: ALGORITMI AVANZATI
-------
The examination consists of a written test (at the same time as the other two module tests) that lasts 1 hour (all tests together last 3 hours). The grade in this module is worth 1/3 of the grade in the Algorithms examination.


Module: COMPLESSITÀ
-------
The examination consists of a written test (at the same time as the other two module tests) that lasts 1 hour (all tests together last 3 hours). The grade in this module is worth 1/3 of the grade in the Algorithms examination.


Module: INTELLIGENZA ARTIFICIALE
-------
The grade in Artificial Intelligence is worth 1/3 of the grade in the Algorithms exam, and it is determined by the grade in a written test.

Bibliografia

Reference texts
Author Title Publishing house Year ISBN Notes
Sanjoy Dasgupta, Christos Papadimitriou, Umesh Vazirani Algorithms (Edizione 1) McGraw-Hill Higher Education 2007 978-0-07-352340-8 Testo secondario
Alan Bertossi Algoritmi e strutture dati (Edizione 1) UTET 2000 88-7750-611-3 Testo secondario
T. Cormen, C. Leiserson, R. Rivest, C. Stein Introduzione agli Algoritmi e Strutture Dati (Edizione 2) McGraw-Hill 2005 88-386-6251-7 Testo consigliato per la prima parte del corso
Steven S. Skiena The Algorithm Design Manual (Edizione 2) Springer 2008 9781848000698 Testo secondario per il corso ma ottimo come manuale di riferimento per un'ampia classe di problemi.
Christos H. Papadimitriou Computational complexity Addison Wesley 1994 0201530821 testo principale
Stuart Russell, Peter Norvig Artificial Intelligence: A Modern Approach (Edizione 2) Prentice Hall 2003 0137903952 Testo adottato
Judea Pearl Heuristics: Intelligent search strategies for computer problem solving (Edizione 1) Addison Wesley 1985 0-201-0559 Testo complementare
Stuart Russell, Peter Norvig Intelligenza artificiale: Un approccio moderno (Edizione 2) Pearson Education Italia 2005 88-7192-22 Traduzione italiana del testo adottato
Chin-Liang Chang, Richard Char-Tung Lee Symbolic Logic and Mechanical Theorem Proving (Edizione 1) Academic Press 1973 0121703509 Testo complementare

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.

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.