University at Buffalo 3 credits Biostatistics
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; designed to discover and model patterns in data. Applications to high-dimensional data and big data will be highlighted. Topics include: Market basket analysis, generalized association rules, recommender systems, hierarchical and prototype-based clustering, self organizing maps, model selection, factor analysis, data visualization, probabilistic graphical models with a focus on Bayesian Networks and Gaussian Graphical Models. This course includes online computational labs, which demonstrate methods and concepts in R, using real data.
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