CSE 326/426 Pattern Recognition (3)


John Spletzer (Spring 2016)

Current Catalog Description

Bayesian decision theory and the design of parametric and nonparametric classifiers: linear (perceptrons), quadratic, nearest-neighbors, neural nets. Machine learning techniques: boosting, bagging. High-performance machine vision systems: segmentation, contextual analysis, adaptation. Students carry out projects, e.g. on digital libraries and vision-based Turing tests. Credit will not be given for both CSE 326 and CSE 426. Prerequisites: CSE 109 and CSE 340 and MATH 205 and (MATH 231 or ECO 045), or consent of instructor.


Sergios Theodoridis and Konstantinos Koutroumbas, "Pattern Recognition", 4th Edition, Elsevier Science & Technology, 2008, ISBN 978-1597492720

Sergios Theodoridis, Aggelos Pikrakis, Konstantinos Koutroumbas, and Dionisis Cavouras, "Introduction to Pattern Recognition: A Matlab Approach", Elsevier Science & Technology, 2010, ISBN 978-0123744869


Students will have


CSE 324 substantially supports the following student enabled characteristics

A. An ability to apply knowledge of computing and mathematics appropriate to the discipline

B. An ability to analyze a problem and identify and define the computing requirements appropriate to its solution

I. An ability to use current techniques, skills, and tools necessary for computing practices

J. An ability to apply mathematical foundations, algorithmic principles, and computer science theory in the modeling and design of computer-based systems in a way that demonstrates comprehension of the tradeoffs involved in design choices

K. An ability to apply design and development principles in the construction of software systems of varying complexity

Major Topics Covered in the Course

  • Matlab review
  • Bayesian classifiers
  • Linear classifiers
  • Nonlinear classifiers
  • Feature selection
  • Feature generation
  • System evaluation
  • Clustering
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