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Machine Learning and Pattern Recognition

Clay Scott Associate Professor University of Michigan - Dept. of EECS
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Machine learning is the study of patterns in large, complex data sets,
with the goal of making quantitative predictions and inferences about
those patterns. In this talk, I will discuss pattern recognition,
perhaps the most fundamental problem in machine learning. Pattern
recognition is the problem of classifying a measured pattern into one
of a finite number of classes, leveraging known examples of each
class. I will overview basic methodological and theoretical issues in
pattern recognition.

Sponsored by

University of Michigan