Pattern Recognition (EE 566)

2022 Fall
Faculty of Engineering and Natural Sciences
Electronics Engineering(EE)
3
10.00
Hüseyin Özkan hozkan@sabanciuniv.edu,
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English
Doctoral, Master
--
Formal lecture,Interactive lecture
Interactive,Discussion based learning,Project based learning,Simulation
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CONTENT

Statistical Pattern Recognition: Parameter Estimation and Supervised Learning, Bayesian Decision Theory, nonparametric approaches (Parzen windows, Nearest Neighbor), Linear Discriminant Functions, Feature extraction/selection; Pattern Recognition via Neural Networks; Syntactic Pattern Recognition; Nonmetric Methods, Unsupervised Learning and Clustering, Hidden Markov Models, Classifier Combination

OBJECTIVE

To give a systematic account of the major topics in pattern recognition, with emphasis on some real world applications

LEARNING OUTCOME

Learn the statistics background on the pattern recognition theory
Learn basics on Bayesian classification; regression; support vector machines; kernel methods
Students will learn both theorical and practical aspects of pattern recognition

ASSESSMENT METHODS and CRITERIA

  Percentage (%)
Final 35
Midterm 25
Assignment 15
Individual Project 25

RECOMENDED or REQUIRED READINGS

Textbook

?Pattern Recognition,? by Sergios Theodoridis, Konstantiros Koutroumbas, Academic Press; 4th Edition ( 2008), ISBN: 9781597492720

Readings

?Pattern Classification,? by Richard O. Duda, Peter E. Hart, and David G. Stork, Wiley-Interscience, 2nd Edition (2000) ISBN: 9780471056690
?Pattern Recognition and Machine Learning, ? by Chris Bishop, Springer, 2007, ISBN: 978-0387310732
?Probability and statistics,? by Morris H. DeGroot, Mark J. Schervish, Addison/Wesley, 3rd Edition (2001), ISBN: 0201524880.
?Mathematical Statistics and Data Analysis (with CD Data Sets),? by John A. Rice, Cengage Learning, 3rd Edition (2006), ISBN: 0534399428