CSC 551 Pattern Recognition
This course provides a broad introduction to pattern recognition. Topics include: Bayesian decision theory, density estimation, linear classifiers, nearest neighbor rules, decision trees, artificial neural networks, dimensionality reduction, feature extraction and feature selection, clustering. The course is directed towards advanced undergraduate and beginning graduate students. Prerequisite: background in probability, statistics, and linear algebra or permission of instructor.
Cross Listed Courses
CSC 451 & CSC 551