Theory and Applications of Pattern Recognition (E C E 532) Syllabus
Course Learning Outcomes
Course Learning Outcome
Students will be able to derive features from a data set using either intuition or standard feature sets.
They will then be able to take a new sample and to classify the sample into one of a number of possible classes.
Students will appreciate the difference between supervised and unsupervised learning.
Students will be able to implement such classification systems in software and to asses the efficacy of their implementations.
Theory and Applications of Pattern Recognition
E C E 532
( 3 Credits )
Pattern recognition systems and components; decision theories and classification; discriminant functions; supervised and unsupervised training; clustering; feature extraction and dimensional reduction; sequential and hierarchical classification; applications of training, feature extraction, and decision rules to engineering problems.
ECE 331 or Math 431 or cons inst
Department: ELECTRICAL AND COMPUTER ENGR College: College of Engineering