N.B. these sheets are defined only since 2007 (agreement of Bologna).
cycle os studies
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
1. Discrete Mathematics, Linear Algebra; 2. Basic Programming and Problem Solving; 3. Programming Languages: C/C++, Python, Matlab, JAVA.
Generic skills to reach
. Competence in analysis and synthesis; . Computer Skills for the scope of the study; . Competence to solve problems; . Capacity of decision; . Critical thinking; . Competence in oral and written communication; . Adaptability to new situations; . Creativity; . Competence in applying theoretical knowledge in practice; . Research skills; (by decreasing order of importance)
Teaching hours per semester
total of teaching hours
Sseminar or study visit
Bibliography of reference
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
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.