Lehigh University

CSE 326 Pattern Recognition (3)

Instructor: Henry Baird

Current Catalog Description

Bayesian decision theory and the design of parametric 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, CSE 340, Math 205, and Math 231, or consent of instructor.

		CSE 326 Pattern Recognition (3) Instructor: Henry Baird Current Catalog Description Bayesian decision theory and the design of parametric 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, CSE 340, Math 205, and Math 231, or consent of instructor.