Computational Genomics

12 units · Undergraduate

Spring: 12 units Dramatic advances in experimental technology and computational analysis are fundamentally transforming the basic nature and goal of biological research. The emergence of new frontiers in biology, such as evolutionary genomics and systems biology is demanding new methodologies that can confront quantitative issues of substantial computational and mathematical sophistication. From the computational side this course focuses on modern machine learning methodologies for computational problems in molecular biology and genetics, including probabilistic modeling, inference and learning algorithms, data integration, time series analysis, active learning, etc. This course counts as a CSD Applications elective Prerequisites: 15-122 Min. grade C and ( 36-326 or 36-226 or 36-218 or 36-235 or 15-259 or 36-225 )