Machine Learning for Healthcare

Undergraduate / Graduate

This course introduces machine learning in healthcare by covering the characteristics of clinical data and the application of machine learning techniques addressing challenges in healthcare and biology. The course will cover tasks such as precision medicine, diagnosis, subtype identification, biomarker identification, risk stratification, disease progression / outcome modeling. To do this, techniques beginning with basic classification and regression and working towards time-series analyses, deep learning, transfer learning, interpretability, causality, and unsupervised learning will be introduced. The first half of the course is problem set focused, and the second half of the course consists of each student proposing and completing a project using real world data (provided), focusing on the complexities of learning from these data and/or using machine learning to improve healthcare. The course will include a couple of guest lectures from Chicago-area clinicians.