RE 602 - Pattern Recognition

3 Credit(s)

A pattern recognition process aims to find a label for each individual input/observation in a specific task or application. One widely known pattern recognition problem is “Classification,” which assigns input data
semantically meaningful class labels based on one or multiple predefined classification criteria. This course covers classical and current theory and practice of supervised and unsupervised classification, including topics of linear and nonlinear classifiers, feature generation, feature selection, template matching, clustering, and application examples. Basic knowledge of calculus, linear algebra, probability theory, and signal
analysis are prerequisites.