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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| Materials Science and Nano Engineering - With Thesis (Pre.Name: Materials Science and Engineering) | * | ||
| Mathematics - With Bachelor's Degree | * | ||
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| Mechatronics Engineering - With Master's Degree | * | ||
| Mechatronics Engineering - With Thesis | * | ||
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| Molecular Biology,Genetics and Bioengineering-With Thesis (Pre.Name:Biological Sciences and Bioeng.) | * | ||
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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 |