University at Buffalo 3 credits Biostatistics
This course presents statistical models for data mining, inference and prediction. The focus will be on supervised learning, which concerns outcome prediction from input data. Students will be introduced to a number of methods for supervised learning, including: linear and logistic regression, shrinkage methods, lasso, partial least squares, tree-based methods, model assessment and selection, model inference and averaging, and neural networks. Computational applications will be presented using R and high dimensional data to reinforce theoretical concepts. This course is the same as EAS 506 and CDA 541, and course repeat rules will apply. Students should consult with their major department regarding any restrictions on their degree requirements.
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