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BSTT 527 - Statistical Learning

Campus: Chicago

Description:

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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