ENEE 712 - Pattern Recognition

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Principles of statistical pattern recognition; hypothesis testing and decision theory; parametric estimation (Bayesian estimation, maximum-likelihood estimation, Gaussian mixture analysis); non-parametric estimation (nearest-neighbor rule and Pazen’s window method); density approximation; linear discriminant functions; feature extraction and selection; feature optimization; neural networks (single-layer perceptrons, multi-layer neural networks); and applications in pattern classification.
Prerequisite: Prerequisites: ENEE 612, ENEE 620 and ENEE 621 or consent of instructor.