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
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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Programs\Type | Required | Core Elective | Area Elective |
Computer Science and Engineering - With Bachelor's Degree | * | ||
Computer Science and Engineering - With Master's Degree | * | ||
Computer Science and Engineering - With Thesis | * | ||
Cyber Security - With Bachelor's Degree | * | ||
Cyber Security - With Master's Degree | * | ||
Cyber Security - With Thesis | * | ||
Data Science - With Thesis | * | ||
Electronics Engineering and Computer Science - With Bachelor's Degree | * | ||
Electronics Engineering and Computer Science - With Master's Degree | * | ||
Electronics Engineering and Computer Science - With Thesis | * | ||
Electronics Engineering - With Bachelor's Degree | * | ||
Electronics Engineering - With Master's Degree | * | ||
Electronics Engineering - With Thesis | * | ||
Energy Technologies and Management-With Thesis | * | ||
Industrial Engineering - With Bachelor's Degree | * | ||
Industrial Engineering - With Master's Degree | * | ||
Industrial Engineering - With Thesis | * | ||
Leaders for Industry Biological Sciences and Bioengineering - Non Thesis | * | ||
Leaders for Industry Computer Science and Engineering - Non Thesis | * | ||
Leaders for Industry Electronics Engineering and Computer Science - Non Thesis | * | ||
Leaders for Industry Electronics Engineering - Non Thesis | * | ||
Leaders for Industry Industrial Engineering - Non Thesis | * | ||
Leaders for Industry Materials Science and Engineering - Non Thesis | * | ||
Leaders for Industry Mechatronics Engineering - Non Thesis | * | ||
Manufacturing Engineering - Non Thesis | * | ||
Manufacturing Engineering - With Bachelor's Degree | * | ||
Manufacturing Engineering - With Master's Degree | * | ||
Manufacturing Engineering - With Thesis | * | ||
Materials Science and Nano Engineering-(Pre:Materials Science and Engineering) | * | ||
Materials Science and Nano Engineering-(Pre:Materials Science and Engineering) | * | ||
Materials Science and Nano Engineering - With Thesis (Pre.Name: Materials Science and Engineering) | * | ||
Mathematics - With Bachelor's Degree | * | ||
Mathematics - With Master's Degree | * | ||
Mathematics - With Thesis | * | ||
Mechatronics Engineering - With Bachelor's Degree | * | ||
Mechatronics Engineering - With Master's Degree | * | ||
Mechatronics Engineering - With Thesis | * | ||
Molecular Biology, Genetics and Bioengineering (Prev. Name: Biological Sciences and Bioengineering) | * | ||
Molecular Biology, Genetics and Bioengineering-(Prev. Name: Biological Sciences and Bioengineering) | * | ||
Molecular Biology,Genetics and Bioengineering-With Thesis (Pre.Name:Biological Sciences and Bioeng.) | * | ||
Physics - Non Thesis | * | ||
Physics - With Bachelor's Degree | * | ||
Physics - With Master's Degree | * | ||
Physics - With Thesis | * |
CONTENT
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
Update Date:
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 |