COM SCI C221
Probabilistic Models in Computational Genomics
Computer Science · 4 units · Graduate courses (200-299)
(Formerly numbered CM221.) Prior knowledge of biology is not required. Designed for engineering students as well as students from biological sciences and medical school. Introduction to probabilistic models in the context of genomics, with emphasis on concepts and inventing new computational and statistical techniques to analyze genomic data. Concurrently scheduled with course C121.
Letter grading.
Requisites
Official UCLA wording
Requisites: course 32 or Program in Computing 10C with grade of C– or better, and one course from Civil and Environmental Engineering 110, Electrical and Computer Engineering 131A, Mathematics 170A, Mathematics 170E, or Statistics 100A.
BruinTree reads · Prerequisite
confidence 1.00 · from UCLA’s structured data- all of
- one of
Requires
Everything that has to come before this course, not just the courses named in the requisite above.
COM SCI C221
- COM SCI 32Introduction to Computer Science II
- COM SCI 31Introduction to Computer Science I
- C&EE 110Introduction to Probability and Statistics for Engineers
- MATH 32ACalculus of Several Variables
Unlocks
What this course is a requisite for, and what those courses lead to in turn.
No course in the catalog lists this as a requisite.