Machine Learning for Public Health

4 units · Graduate courses (200-299)

Lecture, three hours; discussion one hour. Requisite: Biostatistics 100 or 201A or other introductory statistics course. Recommended preparation: environmental health courses and prior coding experience. Project-centric introduction to concepts and applications of machine learning in public health. Students gain hands-on experiences in developing machine-learning models for various applications in public health, including exposure assessment modeling, multiomics, computational toxicology, and disease prediction. Letter grading.