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Principles and design of pattern recognition systems. Statistical classifiers: discriminant functions; bayes, minimum risk, k-nearest neighbors, perceptrons. Clustering and estimation; criteria; k-means, fuzzy, hierarchal, graph- theoretic, simulated and determininstic annealing; maximum likelihood and bayesian methods: nonparametric methods. Overview of applications.

Prerequisites: ECE 130C and 139.

4

Units

Letter

Grading

1, 2, 3

Passtime

None

Level Limit

Engineering

College
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