Machine Learning For Health

2 credits · Allied Health Graduate Program

Data science and machine learning (ML) are now beginning to impact clinical medicine, with performance on some tasks, such as detection of skin cancer, exceeding that of experienced clinicians. This course is designed to introduce students to the data science techniques poised to disrupt clinical practice through foundational material and clinical case studies. Course content will provide students with an intuitive, applications-oriented foundation in these techniques while highlighting both their capabilities and current limitations. Students will be introduced to pitfalls commonly encountered when developing models for clinical data as well as relevant practical and ethical considerations. Prerequisite: An introductory course in statistics and/or probability; and prior use of statistical software (e.g. R, SPSS, SAS, Python) to manage data and run analyses. Prerequisite: None. Credit: 2.