ASRM 442

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Credit: 4 hours.

Introduction to the theory and practice of supervised and unsupervised data analysis techniques. Topics include statistical learning methodologies, cross validation and model selection methods, generalized linear regression, data shrinkage, ridge and lasso methods, decision trees, regression and classification techniques, principal components, unsupervised learning techniques, cluster analysis.

4 undergraduate hours. 4 graduate hours. Credit is not given towards graduation for ASRM 442 and ASRM 451/Stat 432. Prerequisite: ASRM 401 or STAT 400; ASRM 441 or ASRM 450.

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