From Regression to LLMs: Introduction to Machine Learning for Computational Biology

Catalog description

This course will provide a rigorous treatment of computational statistics and machine learning methods used to analyze big biological data types. Analysis methods covered will include: generalized linear models, support vector machines, regularized linear models, kernel methods, random forests, neural networks, large language models. While the course will be focused on analysis methods and connections among methods, applications making use of specific big biological data types will be covered. An understanding of method limitations will be prioritized, as well as how to critically assess when a desired conclusion can be justified. Methods will be implemented in the computer lab in Python, where some previous exposure to programming will be assumed.