CSE 426 - Pattern Recognition

Institution:

Lehigh University

Subject:

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. This course, a version of 326 for graduate students requires advanced assignments. Credit will not be given for both CSE 326 and CSE 426. Prerequisites: CSE 109, CSE 340, Math 205, Math 231, or consent of instructor.

Credits:

3.00

Credit Hours:

Prerequisites:

Corequisites:

Exclusions:

Level:

Instructional Type:

Lecture

Notes:

Additional Information:

Historical Version(s):

Institution Website:

www.lehigh.edu

Phone Number:

(610) 758-3000

Regional Accreditation:

Middle States Association of Colleges and Schools

Calendar System:

Semester

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