Health Data Science III: Supervised Machine Learning in Health

4 Credits

Supervised machine learning is used to create automated systems that sift through labeled/continuous data at high speed to make predictions with minimal human intervention. This course provides students with skills in applying supervised machine learning in contexts of population health. We will cover regression, classification, cross-validation, hyperparameter selection, feature selection, feature engineering, ensemble methods, regularization, and reinforcement learning. Students will learn concepts through hands-on engagement with health data sets, preparing them to contribute effectively to data-driven precision population health. Prerequisites: MATH 052 and BSTA 040