COM SCI 226 · CS 226Machine Learning in Computational Genomics
Computer Science · 4 units · Graduate courses (200-299)
(Formerly numbered M226.) 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 an important toolkit in this endeavor. Biological datasets offer new challenges to the field of machine learning. Examination of statistical and computational aspects of machine learning techniques and their application to key biological questions.
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
Enforced requisite: course 32 or Program in Computing 10C with grade of C– or better.
BruinTree reads · Prerequisite
confidence 1.00 · from UCLA’s structured data- one of
Requires
Everything that has to come before this course, not just the courses named in the requisite above.
COM SCI 226
- COM SCI 32Introduction to Computer Science II
- COM SCI 31Introduction to Computer Science I
- COMPTNG 10CAdvanced Programming
- COMPTNG 10BIntermediate Programming
- COMPTNG 10AIntroduction to Programming
2 direct requisites. The whole upstream is here — 6 courses over 3 levels. 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.