EPIDEM 2191 - ADVANCED THEORY AND METHODS FOR THE ANALYSIS OF EPIDEMIOLOGICAL DATA Minimum Credits: 3 Maximum Credits: 3 This course is an introduction to advanced epidemiologic and statistical methods. The focus of the course is on the fundamental theoretical and applied aspects of using data to answer research questions. Students will be introduced to the causal inference framework and how it relates to standard (e.g., linear and logistic regression) and novel (e.g., inverse probability weighting, g computation, double robust) analytic methods. Students will learn how to use various machine learning (random forest, gradient boosting, support vector machines) approaches for causal effect estimation and predictive analytics. Student will learn how to apply these methods using R. Academic Career: Graduate Course Component: Lecture Grade Component: Grad Letter Grade Course Requirements: Pre-reqs: BIOST 2049 AND EPIDEM 2180 AND EPIDEM 2189 Click here for class schedule information.
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