Machine Learning Applications in Genetics

Description

(Same as Computer Science CM124.) Lecture, four hours; discussion, two hours; outside study, six hours. Requisites: Computer Science 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. Designed for engineering students as well as students from biological sciences and medical school. Introduction to computational analysis of genetic variation and computational interdisciplinary research in genetics. Topics include introduction to genetics, identification of genes involved in disease, inferring human population history, technologies for obtaining genetic information, and genetic sequencing. Focus on formulating interdisciplinary problems as computational problems and then solving those problems using computational techniques from statistics and computer science. Concurrently scheduled with course CM224. Letter grading.

Instructional Format

Primary Format

Lecture

Secondary Format

Discussion

Additional Format

Outside study

Concurrent Course

HUM GEN CM224 - Machine Learning Applications in Genetics

A pair of upper-division undergraduate and graduate courses that share a primary activity. They are scheduled at the same time and place with the same instructor.

Multiple-Listed Courses

COM SCI CM124 - Machine Learning Applications in Genetics

Multiple-listed courses are courses offered jointly by more than one department.

General Catalog 2023-24