Students will expand their statistics and machine learning toolkit by building traditional regression models for continuous and binary data, explore supervised learning methods such as: Tree-based learning, KNN/Collaborative filtering, and Feed forward Neural networks, and understand how to manipulate, ask, and answer questions from big datasets. Students will be expected to propose a population health project mid-semester, and apply and present techniques they learned in class. May be taken in conjunction with BSTA 103.
Notes (github) Notes (nbviewer) Notes (pdf) HW01 (Due=TBA) Class01 Recap Class02 Recap