3.0

credits

Average Course Rating

This course surveys the technical and practical challenges of applying machine learning in healthcare, focusing on two themes: The first theme will cover applications of machine learning to a wide range of healthcare data modalities (e.g., medical imaging, structured health records, etc). Beyond reviewing specific modeling approaches, we will focus on navigating pitfalls in model development and evaluation that arise in a healthcare context. The second theme will cover methodological approaches to developing safe and effective machine learning systems in healthcare, including topics such as (but not limited to) causality, fairness, and distribution shift. This course is designed for students who have a solid existing background in machine learning, and who are interested in both the technical and practical nuances of applying machine learning in healthcare. Grading will be done on the basis of homework assignments as well as a final project. Required course background: EN.601.475/675 Machine Learning or equivalent.