— Machine Learning in Genomics

Prerequisites & course information

Course Description: 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. Learning Activities: Lecture/Discussion 3 hour(s), Project. Grade Mode: Letter. Learning Activities: Lecture/Discussion 3 hour(s), Project. Grade Mode: Letter.