Statistical Pattern Recognition

ENG EC 719

The statistical theory of pattern recognition, including both parametric and nonparametric approaches to classification. Covers classification with likelihood functions and general discriminant function, density estimation, supervised and unsupervised learning, decision trees, feature reduction, performance estimation, and classification using sequential and contextual information, including Markov and hidden Markov models. A project involving computer implementation of a pattern recognition algorithm is required.

SPRG 2017 Schedule

Section Instructor Location Schedule Notes
A1 Saligrama CAS 116 MW 12:20 pm-2:05 pm

Note that this information may change at any time. Please visit the Student Link for the most up-to-date course information.