Pattern Recognition (EE 566)

2022 Fall
Faculty of Engineering and Natural Sciences
Electronics Engineering(EE)
3
10
Hüseyin Özkan hozkan@sabanciuniv.edu,
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English
Doctoral, Master
--
Formal lecture,Interactive lecture
Interactive,Communicative,Discussion based learning,Project based learning,Guided discovery,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 OUTCOMES

  • 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 25
Assignment 50
Individual Project 25

RECOMENDED or REQUIRED READINGS

Textbook

K. P. Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012.

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