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 semestre | Oct 1, 2018 | Jan 31, 2019 |
II semestre | Mar 4, 2019 | Jun 14, 2019 |
Session | From | To |
---|---|---|
Sessione invernale d'esame | Feb 1, 2019 | Feb 28, 2019 |
Sessione estiva d'esame | Jun 17, 2019 | Jul 31, 2019 |
Sessione autunnale d'esame | Sep 2, 2019 | Sep 30, 2019 |
Session | From | To |
---|---|---|
Sessione Estiva | Jul 17, 2019 | Jul 17, 2019 |
Sessione Autunnale | Nov 20, 2019 | Nov 20, 2019 |
Sessione Invernale | Mar 17, 2020 | Mar 17, 2020 |
Period | From | To |
---|---|---|
Sospensione attività didattica | Nov 2, 2018 | Nov 3, 2018 |
Vacanze di Natale | Dec 24, 2018 | Jan 6, 2019 |
Vacanze di Pasqua | Apr 19, 2019 | Apr 28, 2019 |
Festa del Santo Patrono | May 21, 2019 | May 21, 2019 |
Vacanze estive | Aug 5, 2019 | Aug 18, 2019 |
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 Enrollment FAQs
Academic staff
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 enrollment year.
1° Year
Modules | Credits | TAF | SSD |
---|
2° Year activated in the A.Y. 2019/2020
Modules | Credits | TAF | SSD |
---|
3° Year activated in the A.Y. 2020/2021
Modules | Credits | TAF | SSD |
---|
Modules | Credits | TAF | SSD |
---|
Modules | Credits | TAF | SSD |
---|
Modules | Credits | TAF | SSD |
---|
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.
Pattern recognition and Signal and image Processing in Bioinformatics - RICONOSCIMENTO E RECUPERO DELL'INFORMAZIONE PER BIOINFORMATICA (2020/2021)
Teaching code
4S008228
Credits
6
Coordinator
Not yet assigned
Language
Italian
Scientific Disciplinary Sector (SSD)
INF/01 - INFORMATICS
To show the organization of the course that includes this module, follow this link: Course organization
The teaching is organized as follows:
Teoria
Laboratorio
Learning outcomes
The course is aimed at providing the theoretical and applicative 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 techniques of this area will be presented and discussed, in particular linked to representation, classification, clustering and validation. The focus is more on the description of the employed methodologies rather than on the details of applicative 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 two parts:
Theory. This part is devoted to the description and the analysis of the different methodologies for representation, classification and clustering of biological data. Moreover, there will be a more application-oriented part, which 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)
Laboratory. This part is devoted to the implementation, using the MATLAB language, of some of the algorithms analysed in the first two parts.
Detailed Program
Theory:
- Introduction to Pattern Recognition
- Data Representation
- Elements of the Bayes decision theory
- Generative and discriminative classifiers
- Elements of Neural Networks and Hidden Markov Models
- Clustering methods
- Applications
Lab:
- Introduction to matlab
- Data representation and standardization
- Principal Component Analysis
- Gaussians and Gaussian classifiers
- Hidden Markov Models
Bibliography
Activity | Author | Title | Publishing house | Year | ISBN | Notes |
---|---|---|---|---|---|---|
Teoria | P. Baldi, S. Brunak | Bioinformatics, The Machine Learning Approach | MIT Press | 2001 | ||
Teoria | R. Duda, P. Hart, D. Stork | Pattern Classification | Wiley | 2001 | ||
Teoria | C.M. Bishop | Pattern Recognition and Machine Learning | Springer | 2006 | ||
Laboratorio | P. Baldi, S. Brunak | Bioinformatics, The Machine Learning Approach | MIT Press | 2001 | ||
Laboratorio | R. Duda, P. Hart, D. Stork | Pattern Classification | Wiley | 2001 | ||
Laboratorio | C.M. Bishop | Pattern Recognition and Machine Learning | Springer | 2006 |
Examination Methods
See the general notes on the course.
Teaching materials e documents
- Descrizione del corso (it, 88 KB, 05/10/20)
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
Attendance
As stated in the Teaching Regulations for the A.Y. 2022/2023, attendance at the course of study is not mandatory.