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.

Type D and Type F activities

Le attività formative in ambito D o F comprendono gli insegnamenti impartiti presso l'Università di Verona o periodi di stage/tirocinio professionale.
Nella scelta delle attività di tipo D, gli studenti dovranno tener presente che in sede di approvazione si terrà conto della coerenza delle loro scelte con il progetto formativo del loro piano di studio e dell'adeguatezza delle motivazioni eventualmente fornite.

 
I semestre From 10/1/20 To 1/29/21
years Modules TAF Teacher
3° Matlab-Simulink programming D Bogdan Mihai Maris (Coordinator)
II semestre From 3/1/21 To 6/11/21
years Modules TAF Teacher
3° Introduction to 3D printing D Franco Fummi (Coordinator)
3° Python programming language D Vittoria Cozza (Coordinator)
3° HW components design on FPGA D Franco Fummi (Coordinator)
3° Rapid prototyping on Arduino D Franco Fummi (Coordinator)
3° Protection of intangible assets (SW and invention)between industrial law and copyright D Roberto Giacobazzi (Coordinator)
List of courses with unassigned period
years Modules TAF Teacher
1° Subject requirements: mathematics D Rossana Capuani
3° The fashion lab (1 ECTS) D Maria Caterina Baruffi (Coordinator)
3° LaTeX Language D Enrico Gregorio (Coordinator)

Pattern recognition and Signal and image Processing in Bioinformatics - Information recognition and retrieval for bioinformatics (2020/2021)

Teaching code

4S008228

Credits

6

Coordinator

Not yet assigned

Language

Italian

Scientific Disciplinary Sector (SSD)

INF/01 - INFORMATICS

MoodleMoodle Seminars 0

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

The teaching is organized as follows:

Teoria

Credits

4

Period

I semestre

Academic staff

Manuele Bicego

Lessons timetable
Laboratorio

Credits

2

Period

I semestre

Academic staff

Manuele Bicego

Lessons timetable

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

Reference texts
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.

Students with disabilities or specific learning disorders (SLD), who intend to request the adaptation of the exam, must follow the instructions given HERE

Teaching materials e documents