Forum: Course Outlines
Téma: EE 566 - Pattern Recognition [this semester]
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vpa:
EE 566 - Pattern Recognition
Term:Fall Quarter 2004-2005
Coordinator: Aytul Ercil
Textbook: Richard O. Duda, Peter E. Hart, David G. Stork Pattern Classification, 2002
References:Robert Schalkoff, Pattern Recognition, Statistical, Structural and Neural Approaches, 1992
Sergios Theodoridis, Konstantinos Koutroumbas, Pattern Recognition, 1999.
Prerequisites by topic:
1.Ability to use write computer programs in C or use packages like Matlab
2.Basic probability concepts
Topics:
1.Introduction to Pattern Recognition and applications, fundamentals of pattern recognition systems
2.Statistical Pattern Recognition
a.Parametric Estimation and Supervised Learning
b.Bayesian Decision Theory
c.Supervised learning using nonparametric approaches (Parzen windows, Nearest Neighbor)
d.Linear Discriminant Functions
3.Feature extraction/selection
4.Neural Pattern Recognition
5.Syntactic Pattern Recognition
6.Nonmetric Methods
7.Unsupervised Learning and Clustering
8.Hidden Markov Models
9.Combining Classifiers
Assignments:
Several homework assignments for the above topics, some requiring C/Matlab programming, some requiring use ofpackages like Systat, CART.
Projects:
Term project for each student which involves the implementation of pattern recognition techniques for a real-world problem. (10 weeks)
Grading:
Homeworks: % 15
Midterm Exam % 25
Final Exam % 25
Project % 35
David G. Stork: