<p>(Same as Biomathematics M226, Computer Science M226, and Human Genetics M226.) Lecture, four hours; outside study, eight hours. Enforced requisite: Computer Science 32 or Program in Computing 10C with grade of C– or better. 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 important toolkit in this endeavor. Biological datasets offer new challenges to field of machine learning. Examination of statistical and computational aspects of machine learning techniques and their application to key biological questions. Letter grading.</p>

Bioinformatics, Graduate

Combining rigorous, inclusive pedagogy with opportunities for co-curricular activities—including research, internships, and community engagement—the College provides students with diverse learning experiences that helps them thrive, become critical thinkers, and transform society.

College of Letters and Science

Bioinformatics is defined broadly as the study of the inherent structure of biological information.

General Catalog

Machine Learning in Bioinformatics

BIOINFO M226

4 units