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 sem. Oct 1, 2014 Jan 30, 2015
II sem. Mar 2, 2015 Jun 12, 2015
Exam sessions
Session From To
Sessione straordinaria appelli d'esame Feb 2, 2015 Feb 27, 2015
Sessione estiva appelli d'esame Jun 15, 2015 Jul 31, 2015
Sessione autunnale appelli d'esame Sep 1, 2015 Sep 30, 2015
Degree sessions
Session From To
Sessione autunnale appello di laurea 2014 Nov 26, 2014 Nov 26, 2014
Sessione invernale appello di laurea 2015 Mar 18, 2015 Mar 18, 2015
Sessione estiva appello di laurea 2015 Jul 14, 2015 Jul 14, 2015
Sessione autunnale appello di laurea 2015 Nov 25, 2015 Nov 25, 2015
Sessione invernale appello di laurea 2016 Mar 16, 2016 Mar 16, 2016
Holidays
Period From To
Vacanze di Natale Dec 22, 2014 Jan 6, 2015
Vacanze di Pasqua Apr 2, 2015 Apr 7, 2015
Ricorrenza del Santo Patrono May 21, 2015 May 21, 2015
Vacanze estive Aug 10, 2015 Aug 16, 2015

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 L M O P Q T U V Z

Bicego Manuele

manuele.bicego@univr.it +39 045 802 7072

Buffelli Mario Rosario

mario.buffelli@univr.it +39 0458027268

Capaldi Stefano

stefano.capaldi@univr.it +39 045 802 7907

Cicalese Ferdinando

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

Combi Carlo

carlo.combi@univr.it 045 802 7985

Delledonne Massimo

massimo.delledonne@univr.it 045 802 7962; Lab: 045 802 7058

Dominici Paola

paola.dominici@univr.it 045 802 7966; Lab: 045 802 7956-7086

D'Onofrio Mariapina

mariapina.donofrio@univr.it 045 802 7801

Drago Nicola

nicola.drago@univr.it 045 802 7081

Farinelli Alessandro

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

Fiorini Paolo

paolo.fiorini@univr.it 045 802 7963

Franco Giuditta

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

Giachetti Andrea

andrea.giachetti@univr.it +39 045 8027998

Giorgetti Alejandro

alejandro.giorgetti@univr.it 045 802 7982

Giugno Rosalba

rosalba.giugno@univr.it 0458027066

Gobbi Bruno

bruno.gobbi@univr.it

Gregorio Enrico

Enrico.Gregorio@univr.it 045 802 7937

Lovato Pietro

pietro.lovato@univr.it +39 045 802 7035

Manca Vincenzo

vincenzo.manca@univr.it 045 802 7981

Masini Andrea

andrea.masini@univr.it 045 802 7922

Menegaz Gloria

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

Muradore Riccardo

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

Oliboni Barbara

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

Piccinelli Fabio

fabio.piccinelli@univr.it +39 045 802 7097

Posenato Roberto

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

Quaglia Davide

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

Trabetti Elisabetta

elisabetta.trabetti@univr.it 045/8027209

Valenti Maria Teresa

mariateresa.valenti@univr.it +39 045 812 8450

Villa Tiziano

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

Zanatta Marco

marco.zanatta@univr.it +39 045 802 7093

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.

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

4S02716

Credits

12

Coordinatore

Manuele Bicego

Language

Italian

Scientific Disciplinary Sector (SSD)

INF/01 - INFORMATICS

The teaching is organized as follows:

Teoria

Credits

9

Period

I sem.

Laboratorio

Credits

3

Period

I sem.

Academic staff

Pietro Lovato

Learning outcomes

The course is aimed at providing the theoretical and applied basis of Pattern Recognition, a class of automatic methodologies used to recognize and recover information from biological data. In particular, during the course the main aspects of this area will be presented and discussed: representation, classification, clustering and validation. The focus is more on the description of the employed methodologies rather than on the details of application programs (already seen in other courses)

At the end of the course, the students will be able to analyse a biological problem from a Pattern Recognition perspective; the will also have the skills needed to invent, develop and implement the different components of a Pattern Recognition System.

Program

The course generally requires standard skills obtained from other courses of the first two years, with particular emphasis on basic notions of probability, statistics, and mathematical analysis.

The course is divided in three parts:
Part 1. The first part is devoted to the description and the analysis of the different methodologies for representation, classification and clustering of biological data

Part 2. The second part, more application-oriented, is devoted to the critical analysis of some relevant bioinformatics problems which are typically solved with classification or clustering approaches (e.g. gene expression data analysis, medical image segmentation, protein remote homology detection)

Part 3. The third part (in lab) is devoted to the implementation, using the MATLAB language, of some of the algorithms analysed in the first two parts.


Detailed Program

Theory (72 h):
- Introduction to Pattern Recognition
- Data Representation
- Bayes decision theory
- Generative and discriminative classifiers
- Validation
- Neural Networks
- Hidden Markov Models
- Clustering methods
- Clustering validation
- Applications

Lab (36 h):
- Introduction to matlab
- Data representation and standardization
- Principal Component Analysis
- Gaussians and Gaussian classifiers
- Hidden Markov Models

Reference books
R. Duda, P. Hart, D. Stork Pattern Classification. Wiley, 2001
P. Baldi, S. Brunak, Bioinformatics, The Machine Learning Approach. MIT Press, 2001
A.K. Jain and R.C. Dubes, Algorithms for Clustering Data, Prentice-Hall, 1988

Examination Methods

The exam is aimed at the verification of the following skills:
- capability of clearly and concisely describe the different components of a Pattern Recognition System
- capability of analize, understand and describe a Pattern Recognition system (or a given part of it) relative to a biological problem

The exam consists of two parts
i) a written exam containing questions on topics presented during the course (15 points available). The written part is passed is the grade is greater or equal to 8.
ii) an oral presentation of a scientific paper published in relevant bioinformatics journals during 2015. The paper is chosen by the candidate and approved by the instructor (15 points available).

The two parts of the exam can be passed separately: the final grade is the sum of the two grades.
The total exam is passed if the final grade is greater or equal to 18. Each evaluation is maintained valid for the whole academic year.

Teaching materials

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.

Graduation

List of theses and work experience proposals

Stage Research area
Correlated mutations 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.

Career management


Area riservata studenti