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MATH-656 Data Exploration and Data Mining
Spring only
G. Wilson
This course presents an
introduction to computational and statistical methods for exploring
large data sets and discovering patterns in them. Graphical exploration, feature extraction and dimension reduction, data tours, clustering, finite mixture models, expectation maximization, multivariate
visualization, model selection in linear regression, logistic regression, decision trees, cross validation and bootstrap, bagging and
boosting. Software (Matlab, SAS Enterprise Miner) will be used throughout the course.
Fall semester.
Credits: 3
Prerequisites: None
Course syllabi
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Spring '10:
Wilson G
(description)
Additional syllabi may be available in prior academic years.
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