{"id":723,"date":"2015-12-30T09:50:14","date_gmt":"2015-12-30T11:50:14","guid":{"rendered":"http:\/\/www.inf.ufrgs.br\/profcomp_wp\/?page_id=723"},"modified":"2016-05-12T15:58:26","modified_gmt":"2016-05-12T18:58:26","slug":"cmp265","status":"publish","type":"page","link":"https:\/\/www.inf.ufrgs.br\/profcomp\/lista-de-disciplinas\/cmp265\/","title":{"rendered":"CMP265"},"content":{"rendered":"

Pattern Recognition Methods and Applications<\/strong><\/h3>\n

Professor<\/b>: Jacob Scharcanski<\/a>
\nPrerequisites<\/b>: –
\nHours<\/b>: 60 hs
\nCredits<\/b>: 4
\nSemesters<\/b>: Second semester
\nUndergraduate Enrollment<\/b>: The enrollment must be made as Special Student
\nPage Link<\/b>: –<\/p>\n

SUMMARY<\/strong><\/p>\n

Introduction to Pattern Recognition; Bayesian decision theory; Maximum likelihood and
\nBayesian estimation; Non-parametric techniques and linear discriminant function;
\nStochastic and pattern recognition methods; graphic (graph-based) models; Clustering.<\/p>\n

OBJECTIVES<\/strong><\/p>\n

This course will introduce the fundamentals of statistical pattern recognition with examples
\nfrom several application areas. Techniques for handling multidimensional data of various
\ntypes and scales along with algorithms for clustering and classifying data will be explained.
\nThis is an graduate level course suited for graduate students in Computer Science and
\nEngineering. It is primarily intended for highly motivated graduate students who are
\ninterested in doing research in the areas of Pattern Recognition, Artificial Intelligence,
\nImage Processing, Graphics and Computer Vision. Many open problems in these areas
\nsuitable for investigation by Master’s or Ph.D. students will be presented and proposed.<\/p>\n

PROGRAM<\/strong><\/p>\n

\u2022 Introduction to Pattern Recognition
\n\u2022 Bayesian Decision Theory
\n\u2022 Maximum Likelihood and Bayesian Estimation
\n\u2022 Non-Parametric Techniques and Linear Discriminant Functions
\n\u2022 Stochastic Methods and Pattern Recognition (Selected Topics)
\n\u2022 Graphic (Graph-Based) Models in Pattern Recognition
\n\u2022 Clustering (Selected Topics)<\/p>\n

Prerequisites:
\nBasic knowledge of probability and statistics, strong programming skills (e.g. MATLAB),
\nand linear algebra. Courses on artificial intelligence, image processing, computer vision,
\nand machine learning would be helpful but not required.<\/p>\n

EVALUATION<\/strong><\/p>\n

Grading will be based on three projects involving programming and presentation. The
\nmaterial covered by the projects will be drawn from the material covered in class. The
\ntopics of the projects should be related to the application of a pattern recognition technique
\n(i.e., preferably state-of-the-art) to practical problems (e.g. artificial intelligence or
\ncomputer vision). Each presentation should be professional as if it was presented in a
\nformal conference (i.e., slides\/projector). Late projects will not be accepted after the second
\nclass following the deadline (the project weight will be decreased by 50% if delivered in
\nthe first class after the deadline). If you are unable to deliver a project by the deadline, you
\nmust discuss it with the instructor before the deadline. Please, note that good programming
\nskills and strong motivation would be essential in order to complete the projects.<\/p>\n

BIBLIOGRAPHY<\/strong><\/p>\n

\u2022 R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, Wiley-Interscience.
\n2001.
\nComplementary :
\n\u2022 K. Fukunaga, Statistical Pattern Recognition, 2nd edition, Morgan Kaufmann, 1990
\n\u2022 T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning: Data Mining,
\nInference and Prediction, Springer-Verlag, 2009.
\n\u2022 Selected papers (e.g.: IEEE PAMI, CVPR, etc.)<\/p>\n","protected":false},"excerpt":{"rendered":"

Pattern Recognition Methods and Applications Professor: Jacob Scharcanski Prerequisites: – Hours: 60 hs Credits: 4 Semesters: Second semester Undergraduate Enrollment: The enrollment must be made as Special Student Page Link: – SUMMARY Introduction to Pattern Recognition; Bayesian decision theory; Maximum likelihood and Bayesian estimation; Non-parametric techniques and linear discriminant function; Stochastic and pattern recognition methods; […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":462,"menu_order":265,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"_links":{"self":[{"href":"https:\/\/www.inf.ufrgs.br\/profcomp\/wp-json\/wp\/v2\/pages\/723"}],"collection":[{"href":"https:\/\/www.inf.ufrgs.br\/profcomp\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.inf.ufrgs.br\/profcomp\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.inf.ufrgs.br\/profcomp\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.inf.ufrgs.br\/profcomp\/wp-json\/wp\/v2\/comments?post=723"}],"version-history":[{"count":4,"href":"https:\/\/www.inf.ufrgs.br\/profcomp\/wp-json\/wp\/v2\/pages\/723\/revisions"}],"predecessor-version":[{"id":2484,"href":"https:\/\/www.inf.ufrgs.br\/profcomp\/wp-json\/wp\/v2\/pages\/723\/revisions\/2484"}],"up":[{"embeddable":true,"href":"https:\/\/www.inf.ufrgs.br\/profcomp\/wp-json\/wp\/v2\/pages\/462"}],"wp:attachment":[{"href":"https:\/\/www.inf.ufrgs.br\/profcomp\/wp-json\/wp\/v2\/media?parent=723"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}