Middle East Technical University

Electrical-Electronics Engineering Department

EE 583 Pattern Recognition

FALL 2013   Course Syllabus

Instructor:      Prof. A. Aydın Alatan (Room C-204, (2351, alatan@eee.metu.edu.tr)

Course web page:  http://www.eee.metu.edu.tr/~alatan/Courses/proutline.html

Textbook: Pattern Classification, R.O. Duda, P.E. Hart & D.G. Stork, 2^nd. edition, Wiley Inc., 2001

Reference books:

• Pattern Recognition and Machine Learning, C. M. Bishop, Springer 2006
• Pattern Recognition, 4^th ed., Theodoridis & Koutroumbas, 2009

Internet resources (as of October 2013):

• UC Irvine Machine Learning Repository (Datasets) : http://archive.ics.uci.edu/ml/
• Pattern Recognition Tools : http://www.37steps.com/
• OpenPR - Open Pattern Recognition Project : http://www.openpr.org.cn/
• Statistical Pattern Recognition Toolbox :  http://cmp.felk.cvut.cz/cmp/software/stprtool/
• Challenges in Machine Learning : http://www.chalearn.org
• Tutorials in Learning: http://homepages.inf.ed.ac.uk/rbf/IAPR/researchers/MLPAGES/mltut.htm
• Deep Learning : http://deeplearning.net/

Grading:         % 25 Midterm (closed book)

                        % 35 Final (closed book)

                        % 30 Term Project

                        % 10 Homework

Outline: Introduction

                                    Overview, basic concepts, various Approaches

                        Statistical Pattern Recognition

Bayes Decision Theory

Supervised Learning: Parametric & Non-parametric approaches

Linear Discriminant Functions

Unsupervised Learning

                        Non-metric methods

Decision trees

Grammars, Parsing, Inference by syntactic methods

                        Graphical models for Structural Pattern Recognition

Graph matching, relational and attributed graphs

Neural Pattern Recognition

Feedforward & non-feedforward networks.

                        Deep Learning Paradigm