36-470 Special Topics: Statistical Methods in Health Sciences

9 units, Carnegie Mellon University

As the volume of health and clinical data continues to expand, the integration of statistical and machine learning methods becomes increasingly important for enhancing healthcare efficiency. However, there are challenges in modeling health data, for example, annotated data is often limited or subject to incompleteness. In this course, we will introduce statistical methods that address these challenges, including survival analysis, latent variable models, clustering, semisupervised learning, and so on. An emphasis will put on understanding methodological foundations and how to appropriately apply methods to health data. Through homework assignments, labs, paper presentations, and a final project, students will gain handsonexperience in applying statistical methods to solve problems arising from health sciences.

Prerequisites: 36-401 (min C)

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