Machine Learning with Biomedical Data

Graduate / Professional

This course covers many popular topics in machine learning and statistics that are widely used for the exploration of biomedical data. Techniques covered include different linear prediction methods, random forest, boosting, neural networks, and some recent progress on model inference in high dimensions, as well as dimension reduction and clustering. Various examples using biomedical data—e.g., microarray gene expression data, single-cell RNA-Seq data—are provided. The emphasis is on the statistical aspects of different machine-learning methods and their applications to problems in computational biology. Prerequisites: S&DS 542 (or S&DS 612) and BIS 623 (or S&DS 612). This course assumes prior knowledge of statistical inference and regression. It also involves programming, and knowledge of R or Python is required. T 7pm-8:50pm