Machine Learning in Computational Genomics

(Formerly numbered M226.) Recommended: one course from Biostatistics 100A, 110A, Civil Engineering 110, Electrical and Computer Engineering 131A, Mathematics 170A, or Statistics 100A. Familiarity with probability, statistics, linear algebra, and algorithms expected. Designed for engineering students as well as students from biological sciences and medical school. Biology has become data-intensive science. Bottleneck in being able to make sense of biological processes has shifted from data generation to statistical models and inference algorithms that can analyze these datasets. Statistical machine learning provides an important toolkit in this endeavor. Biological datasets offer new challenges to the field of machine learning. Examination of statistical and computational aspects of machine learning techniques and their application to key biological questions.

Letter grading.