Prerequisite: SPHBS 730. The course is structured to provide a rigorous and practical introduction to core machine learning methods, with a primary emphasis on both supervised and unsupervised learning techniques. Students will learn fundamental concepts such as regression, classification, clustering, dimensionality reduction, model evaluation, and bias-variance tradeoff, focusing on their relevance and utility in analyzing real-world biomedical and public health data. Throughout the semester, students will have several opportunities for hands-on learning through coding exercises and projects using real datasets. Class examples and labs will be presented using the R software, ensuring that students gain practical computational skills essential for data analysis in public health research. However, some content and resources will also be provided in Python to broaden students’ programming perspectives. To enrich the course and broaden students’ exposure to the latest advances in the field, guest lectures will be offered on topics at the frontier of AI and biomedical data science. These sessions will introduce students to cutting-edge areas such as deep learning, natural language processing with large language models (LLMs), image analysis, and graph-based machine learning. The aim of these guest lectures is to provide insights into specialized and emerging domains, inspire students to pursue advanced study, and highlight the diverse applications of machine learning in public health beyond traditional supervised and unsupervised methods. Each week students will participate in both lecture and lab work within the scheduled class session.

Note that this information may change at any time. Please visit the MyBU Student Portal for the most up-to-date course information.