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This course introduces the theory and practice of statistical pattern recognition, emphasizing supervised and unsupervised learning methods. Topics include Bayesian statistics and classification, feature design and selection, dimensionality reduction, and clustering. Students apply modern algorithms through hands-on programming and a term project, gaining experience in extracting meaningful information from complex data sets.

College/Department: College of Engineering and Computing/Electrical Computer Engr
Repeat Status: Not repeatable for credit
Prerequisites: ECE 3700 [Min Grade: D] or ECE 361 [Min Grade: D]