COM SCI C121 · CS C121Probabilistic Models in Computational Genomics
Computer Science · 4 units · Undergraduate upper division (100-199)
(Formerly numbered CM121.) 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 C221.
Letter grading.
When it runs
Not on the schedule for any of Fall 2025 through Spring 2027. UCLA publishes only that window, so this does not mean the course is gone — check the official listing.
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 C121
- COM SCI 32Introduction to Computer Science II
- COM SCI 31Introduction to Computer Science I
- C&EE 110Introduction to Probability and Statistics for Engineers
- COMPTNG 10CAdvanced Programming
- COMPTNG 10BIntermediate Programming
- COMPTNG 10AIntroduction to Programming
- EC ENGR 131AProbability and Statistics
- MATH 170AProbability Theory I
- MATH 131AAnalysis
- STATS 100AIntroduction to Probability
- MATH 170EIntroduction to Probability and Statistics 1: Probability
7 direct requisites. Showing 30 courses over 3 levels; the branches marked with a count carry on past it. Every course here opens its own tree.
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.