CLRES 2035 - FUNDAMENTALS OF MACHINE LEARNING IN CLINICAL RESEARCH

Minimum Credits: 1
Maximum Credits: 1
In this introductory-level course we will cover the main concepts of statistical machine learning, including the theoretical aspect of generalization properties where a model is applied to unseen data, and the practical aspect of applying state-of-the-art models to static and dynamic problems in classification, regression or density estimation. Examples of real-life applications in health and biomedical sciences will be used to illustrate the interest in statistical machine learning. The course does not require advanced knowledge in mathematics or programming. All computations will be done in Stata. The lectures will focus on the essential elements of modern data analysis methods with minimum use of mathematical formulars. Real-life applications will be used to illustrate the interest of causal inference in clinical research.
Academic Career: Graduate
Course Component: Lecture
Grade Component: Grad LG/SNC Basis

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