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In completing the course, the student shall be able to:
CLO1. formulate and describe various applications in pattern recognition, understand the Bayesian approach to pattern recognition
CLO1. be able to mathematically derive, construct, and utilize Bayesian-based classifiers, and non-Bayesian classifiers both theoretically and practically.
CLO1. be able to identify the strengths and weaknesses of different types of classifiers
CLO2. understand basic concepts such as the central limit theorem, the curse of dimensionality, the bias-variance dilemma, and cross-validation
CLO3. validate and assess different clustering techniques
CLO4. apply various dimensionality reduction methods whether through feature selection or feature extraction
CLO5. assess classifier complexity and regularization parameters
CLO5. be able to combine various classifiers using fixed rules or trained combiners and boost their performance
CLO6. implement simple classification methods for some special tasks such as face recognition8 digit recognition and understand the possibilities and limitations of pattern recognition