CISC 432 - Statistical Pattern Recognition

(3 semester hours)
Prerequisites: MATH 280 and 60 credit hours completed
Description: Many emerging applications, such as indexing, security, forensics, and information discovery, involve the use of novel ideas and effective techniques in teaching computers to recognize patterns in various signals and data, ranging from documents, images, audio, and other sensory signals. This course includes the introduction to basic theories, algorithms, and practical solutions of statistical pattern recognition. Topics covered include feature extraction, feature selection, Bayesian classifiers, neural networks, discriminative classifiers, clustering, performance evaluation, and fusion of models. The student gets some hands-on experience in the design, implementation and evaluation of pattern recognition algorithms by applying them to real-world problems. Offered Fall Semester, annually.