Covers statistical/machine learning methods including supervised learning (e.g., Support Vector Machines, Decision Trees, Random Forest, Boosting) and unsupervised learning with a focus on their application to public health. Course Information: Extensive computer use required. Prerequisite(s): BSTT 401 and BSTT 505 and BSTT 523; or BSTT 523 and BSTT 525 and basic understanding of R programming, or consent of the instructor. Recommended background: IPHS 402 or EPID 406 or BSTT 494.