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Objective   The main objective of this course is to allow our graduate students to understand the concepts of probability and facilitate the use of probability tools to solve problems that...
Course Description:   Covers basic concepts of pattern recognition systems, application examples, PDF estimation, maximum likelihood estimation, Bayesian estimation, KNN estimation,...
Course Description (catalog):   Fundamentals of digital image processing, basics of image enhancement in spatial and frequency domain, image restoration, color image processing, image...
Multiple‐criteria decision problems involve selecting an alternative that best satisfies a collection of criteria as quantified by a scalar corresponding to an aggregation of the...