Machine Learning with Applications to Omics Data

Catalog description

This course introduces fundamental theory and applications of machine learning methods in the context of omics data, including genomics and other complex biological datasets. Students will explore both the detailed theoretical foundations and practical applications of a range of supervised and unsupervised learning techniques. Key topics include linear models, discriminant analysis, support vector machines, tree-based methods, and model assessment techniques. The course also includes coding lab sessions that provide hands-on experience with instructor-recommended programming languages and prepare students to apply machine learning models to biomedical research challenges.