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ECES 523 Detection & Estimation Theory 3.0 Credits

This course introduces the field of detection and estimation and provides tools for classifying and learning about patterns in the face of total, partial or incomplete prior knowledge. Topics covered include Bayes classifier; Parametric estimation and supervised learning (MLE and Bayes Learning); Hypothesis testing; Decision Fusion; Unsupervised learning; and Non parametric testing.

College/Department: College of Engineering
Repeat Status: Not repeatable for credit
Prerequisites: ECES 521 [Min Grade: C]

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