RE 405 - Introduction to Pattern Recognition and Machine Learning

3 Credit(s)

This course introduces basic concepts, principles, algorithms, and applications of pattern recognition and machine learning, and provides a foundation for advanced study in topics shared by machine learning, statistical inference, and signal processing. Topics includes linear and nonlinear classifiers, feature extraction and selection, Bayesian learning, density estimation, clustering, neural networks, and application examples.
Prerequisite(s): ENGR 315 and RE 301 or EE 346