PHAR 5227 Supervised Machine Learning

2 SCH. In this course students will learn to apply machine learning methodologies to health outcomes research. The course will emphasize applications, analyses, and interpretation rather than mathematical and statistical theories or formula. Students will learn the differences between statistical and supervised machine learning methods. The course will cover supervised algorithms such as decision trees, boosting methods, and neural networks and hands on training with appropriate software (example: R, Python).
Prerequisite: Enrollment in the MS-AOR Program
Offered Summer
Letter Grade