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BIOSTAT 707 - Statistical Methods for Learning and Discovery

This course surveys a number of techniques for high dimensional data analysis useful for data mining, machine learning and genomic applications, among others. Topics include principal and independent component analysis, multidimensional scaling, tree-based classifiers, clustering techniques, support vector machines and networks, and techniques for model validation. Core concepts are mastered through the analysis and interpretation of several actual high dimensional genomics datasets. Prerequisites: BIOSTAT 701, 702, 704, 705, and 721 or 722 or permission of The Director of Graduate Studies. Credit: 2
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Fall 2020, Fall 2019, Fall 2018, Fall 2017
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