Machine Learning Approaches in Biological Sciences

Catalog description

A wide range of high-throughput technologies are now being used to generate data to answer an ever-increasingly diverse set of questions about biological systems. The next great challenge is integrating data analysis in a systems biology approach that utilizes novel supervised machine learning methods, which accommodate heterogeneity of data, are robust to biological variation, and provide mechanistic insight. The course will not focus on detailed mathematical models, but instead on how these machine learning tools may be used to analyze biological data, in particular gene and protein expression.