DEPARTAMENTO DE FÍSICA

 

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Ano letivo: 2006-2007
Especificação técnica - ficha curricular
N.B. estas fichas estão definidas apenas desde 2007 (acordo de Bolonha).

Elementos especificos
código da disciplinaciclo de estudossemestre lectivocréditos ECTSlíngua de ensino
26en


Objectivos formativos
Since early times of computing the design and implementation of algorithms emulating the human
ability to recognize patterns has been found a most intriguing and challenging task.
Pattern Recognition (PR) is the scientific discipline that studies the operation and design of systems that
recognize patterns in data. Important application areas in Computer Science are:
1. Person Identification;
2. Facial Expression Detection;
3. Vehicle Trajectory Recognition;
4. Object Recognition;
5. Mouse Recognition;
6. Handwriting Character Recognition;
7. Speech Analysis;
8. Strategic Games;
9. Pattern Mining in the WEB (WWW, DataWarehouses, Business Intelligence, etc.);
10. Biomedical Data Mining
Programa genérico mínimo
Pattern Recognition: Concepts, Methods and Applications.
1.Introduction;
2.Statistical Approaches;
3.Neural Networks;
4.Support Vector Machines *
5.Structural Pattern Recognition;
6.Pattern Recognition Project;*
7.Pattern Recognition Software.
Pré-requisitos
1. Discrete Mathematics, Linear Algebra;
2. Basic Programming and Problem Solving;
3. Programming Languages: C/C++, Python, Matlab, JAVA.
Competências genéricas a atingir
. Competência em análise e síntese;
. Conhecimentos de informática relativos ao âmbito do estudo;
. Competência para resolver problemas;
. Capacidade de decisão;
. Competência em raciocínio crítico;
. Competência em comunicação oral e escrita;
. Adaptabilidade a novas situações;
. Criatividade;
. Competência em aplicar na prática os conhecimentos teóricos;
. Competência em investigar;
(por ordem decrescente de importância)
Horas lectivas semestrais
aulas teóricas30
seminário30
outras actividades2
total horas lectivas62

Método de avaliação
Relatório de seminário ou visita de estudo25 %
Projecto35 %
Exame40 %

Bibliografia de referência
Main Bibliography
Marques de Sá, J.P.(2001), Pattern Recognition: Concepts, Methods and Applications, Springer-Verlag.
http://www.amazon.com/Pattern-Recognition-Concepts-Methods-Applications/dp/3540422978
Complementar Bibliography
Duda, R. O., Hart, P.E., and Stork, D.G. (2001). Pattern Classification, 2nd ed. Wiley Interscience, ISBN: 0-471-05669-3.
Bishop, C.M. (2006). Pattern Recognition and Machine Learning, Springer Verlag
Practical Introduction to Matlab,
http://www.math.mtu.edu/~msgocken/intro/intro.html
Jorge Salvador Marques (2005), Reconhecimento de Padrões: Métodos Estatísticos e Neuronais, 2nd
Ed.,ISBN: 972-8469-08-X,
http://istpress.ist.utl.pt/lrecpad.html
Software:
Statistical Pattern Recognition Toolbox (SPRTool)
http://cmp.felk.cvut.cz/cmp/software/stprtool/
PRTools: The Matlab Toolbox for Pattern Recognition
http://www.prtools.org/
MBP Neural Network Tools
http://dit.ipg.pt/MBP/
Matlab Tutorial
http://www.math.utah.edu/lab/ms/matlab/matlab.html
MATLAB Primer
http://www.math.ucsd.edu/~bdriver/21d-s99/matlab-primer.html
Datasets:
Machine Learning DATASETS
http://archive.ics.uci.edu/ml/
PRTools DataSets
http://eden.dei.uc.pt/~bribeiro/PRTools.rar
Electronic References of Pattern Recognition University Courses on the Web
http://eden.dei.uc.pt/~bribeiro/TRP2010-2011/TRP_Electronic_References.html
Pattern Recognition on the WEB,
http://cgm.cs.mcgill.ca/~godfried/teaching/pr-web.html
Método de ensino
Theoretical classes.
Practical Lab Classes.
Seminars.
Pattern Recognition Techniques (TRP) will work partially via Moodle Platform (Foruns, News, Seminars
Discussion, Project Discussion) Link: http://classes.dei.uc.pt/course/view.php?id=16
Above components are essential to successfully obtain the competences and reach the goals of a
Pattern Recognition Course. Namely, the requirements for the design and implementation of the TRP
Project are quite demanding and strongly need to be supported by the learning/teaching methods
described above.
Recursos específicos utilizados