BST 227

Machine Learning in Genomics

Emerging problems in molecular biology and current machine learning-based solutions to those problem. How deep learning, kernel methods, graphical models, feature selection, non-parametric models and other techniques can be applied to application areas such as gene editing, gene network inference and analysis, chromatin state inference, cancer genomics and single cell genomics.

Units: 4.0

Hours: Lecture/Discussion—3 hour(s); Project (Term Project).