BIOST 2155
Introductory Statistical Learning for Health Sciences
A graduate course introducing the foundations of supervised and unsupervised statistical learning for second-year MS Biostatistics and early-PhD students. Topics span regularized regression, classification (including support vector machines and generative models), clustering, dimensionality reduction, decision trees, and ensemble methods, with hands-on R implementations on health-science datasets.
From Fall 2027, this course will be relaunched as BIOST 2183: Health Data Science — Statistical Learning and Artificial Intelligence, an expanded 3-credit version that retains the classical statistical-learning core and adds Python, scikit-learn, and PyTorch-based deep-learning modules for biomedical data.