University at Buffalo 3 credits Computational Data-Enabled Sci
This course presents the topic of data mining from a statistical perspective, with attention directed towards both applied and theoretical considerations. An emphasis will be placed on unsupervised learning methods, especially those designed to discover and exploit hidden structures in high-dimensional data. Topics include: hierarchical and center-based clustering, principal component analysis, data visualization, random forests, directed and undirected graphical models, and special considerations when n>>p. Computational applications to high-dimensional data will be presented using Matlab and R to illustrate methods and concepts. This course is the same as STA 546 and EAS 507 and course repeat rules will apply. Students should consult with their major department regarding any restrictions on their degree requirements.
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