Machine Learning Applications in Genetics

Course Description

(Formerly numbered CM124.) Lecture, four hours; discussion, two hours; outside study, six hours. Requisites: course 32 or Program in Computing 10C with grade of C– or better, Mathematics 33A, and one course from Civil Engineering 110, Electrical and Computer Engineering 131A, Mathematics 170A, Mathematics 170E, or Statistics 100A. Prior knowledge of biology is not required. Introduction of main applications of machine learning in genetics. Students are prepared for interdisciplinary research in genetics that involves a major computational and statistical component. Topics include introduction to genetics, identification of genes involved in disease using regression techniques, inference of heritability using linear mixed models, inferring human population history using Markov models, methods for phasing genotype data including expectation maximization, computational optimization methods and methods for dimensionality reduction including principal component analysis (PCA), and genotype-phenotype prediction using machine learning techniques. Concurrently scheduled with course C224. Letter grading.

Code

COM SCI C124

Credits

4

Subject

Computer Science

Programs That Include This Course

• Bachelor's Computational Biology BS
• Bachelor's Human Biology and Society BA
• Bachelor's Human Biology and Society BS
• Bachelor's Microbiology, Immunology, and Molecular Genetics BS
• Bachelor's Molecular, Cell, and Developmental Biology BS
• Minor Bioinformatics Minor
• Minor Data Science Engineering Minor
• Minor Evolutionary Medicine Minor