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.
Course calendar
The Academic Calendar sets out the degree programme lecture and exam timetables, as well as the relevant university closure dates..
| Period | From | To |
|---|---|---|
| I sem. | Oct 1, 2014 | Jan 30, 2015 |
| II sem. | Mar 2, 2015 | Jun 12, 2015 |
| 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 |
| 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 |
| 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.
Should you have any doubts or questions, please check the Enrolment FAQs
Academic staff
alberto.benvegnu@univr.it
+39 045 802 7072
bruno.gobbi@univr.it
Ugolini Simone
simone.ugolini@univr.it
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.
| Modules | Credits | TAF | SSD |
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| Modules | Credits | TAF | SSD |
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| Modules | Credits | TAF | SSD |
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1° Year
| Modules | Credits | TAF | SSD |
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2° Year activated in the A.Y. 2015/2016
| Modules | Credits | TAF | SSD |
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3° Year activated in the A.Y. 2016/2017
| Modules | Credits | TAF | SSD |
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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.
Information recognition and retrieval for bioinformatics (2016/2017)
Teaching code
4S02716
Credits
12
Language
Italian
Scientific Disciplinary Sector (SSD)
INF/01 - INFORMATICS
The teaching is organized as follows:
Teoria
Laboratorio
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 e documents
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10. Clustering Validazione
(it, 316 KB, 11/11/16)
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11. Hidden Markov Models
(it, 1032 KB, 21/11/16)
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12. Reti Neurali
(it, 500 KB, 21/11/16)
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13. Applicazioni - parte 1
(it, 6157 KB, 19/12/16)
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14. Applicazioni - parte 2
(it, 6974 KB, 19/12/16)
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7. Introduzione al Clustering
(it, 423 KB, 11/11/16)
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8. Clustering - similarità
(it, 254 KB, 11/11/16)
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9. Metodologie di clustering
(it, 798 KB, 11/11/16)
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Istruzioni per il seminario
(it, 56 KB, 07/11/16)
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SeminariAssegnati
(it, 42 KB, 25/09/17)
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1. Introduzione
(it, 5094 KB, 03/10/16)
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2. Rappresentazione
(it, 10178 KB, 03/10/16)
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3. Teoria della decisione di Bayes
(it, 546 KB, 11/10/16)
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4. Classificatori generativi
(it, 2444 KB, 11/10/16)
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5. Classificatori discriminativi
(it, 1148 KB, 11/10/16)
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6. Validazione dei classificatori
(it, 324 KB, 11/10/16)
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Lab 01 - Intro Matlab
(it, 2160 KB, 03/10/16)
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Lab 01 - Soluzioni
(it, 1 KB, 17/10/16)
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Lab 02 - Intro Matlab 2
(it, 1061 KB, 10/10/16)
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Lab 02 - Soluzioni
(it, 3 KB, 17/10/16)
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Lab 03 - Soluzioni
(it, 2 KB, 24/10/16)
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Lab 03 - Standardizzazione, PCA
(it, 325 KB, 17/10/16)
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Lab 04 - Gaussiane
(it, 190 KB, 24/10/16)
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Lab 04 - Soluzioni
(it, 4 KB, 07/11/16)
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Lab 05 - Parzen Windows
(it, 256 KB, 07/11/16)
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Lab 05 - Soluzioni
(it, 5 KB, 14/11/16)
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Lab 06 - KNN
(it, 279 KB, 14/11/16)
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Lab 06 - Soluzioni
(it, 23 KB, 21/11/16)
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Lab 07 - PRTools 1
(it, 880 KB, 21/11/16)
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Lab 07 - Soluzioni
(it, 0 KB, 28/11/16)
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Lab 08 - PRTools 2
(it, 130 KB, 28/11/16)
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Lab 08 - Soluzioni
(it, 0 KB, 12/12/16)
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Lab 09 - Kmeans
(it, 306 KB, 12/12/16)
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Lab 09 - Soluzioni
(it, 1 KB, 19/12/16)
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Lab 10 - HMM
(it, 703 KB, 19/12/16)
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Lab 10 - Soluzioni
(it, 277 KB, 09/01/17)
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Lab 11 - Ripasso
(it, 268 KB, 09/01/17)
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: only in this way will you be able to receive notification of all the notices from your teachers and your secretariat via email and soon also via the Univr app.
Graduation
List of theses and work experience proposals
| Stage | Research area |
|---|---|
| Correlated mutations | Various topics |
Attendance
As stated in the Teaching Regulations for the A.Y. 2022/2023, attendance at the course of study is not mandatory.
Please refer to the Crisis Unit's latest updates for the mode of teaching.
