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BSTT 527 - Statistical Learning

Campus: Chicago


Covers multivariate statistical methods such as LASSO, ElasticNet, Decision Trees etc, and machine learning methods Bagging, random Forest, Boosting etc in context of statistical learning in PH applications. Course Information: Extensive computer use required. Prerequisite(s): IPHS 402 and BSTT 505; or BSTT 523 and BSTT 525. Recommended Background: IPHS 402 or EPID 406 or BSTT 494.

Option 1

Number of Required Visit(s): 0

Course Level: Graduate

Credit: 3

Term(s): Fall


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