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Subject name: Pattern Recognition |
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Author : Topic content : |
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In completing the course, the student shall be able to: • formulate and describe various applications in pattern recognition, understand the Bayesian approach to pattern recognition • be able to mathematically derive, construct, and utilize Bayesian-based classifiers, and non-Bayesian classifiers both theoretically and practically. • be able to identify the strengths and weaknesses of different types of classifiers • understand basic concepts such as the central limit theorem, the curse of dimensionality, the bias-variance dilemma, and cross-validation • validate and assess different clustering techniques CLO4. apply various dimensionality reduction methods whether through feature selection or feature extraction • assess classifier complexity and regularization parameters • be able to combine various classifiers using fixed rules or trained combiners and boost their performance • implement simple classification methods for some special tasks such as face recognition , digit recognition and understand the possibilities and limitations of pattern recognition
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