PHYSICS C270M
Machine Learning for Physical Sciences Laboratory
Physics · 4 units · Graduate courses (200-299)
Project-based course designed for students with no previous experience in machine learning to learn about methods and algorithms in machine learning and their application to scientific problems in physical sciences. Development of experience in compilation, analysis, and cleaning of data. Machine learning topics include classification, regression, dimensionality reduction, clustering, and kernel methods. Concurrently scheduled with course C170M.
S/U or letter grading.
Requisites
Official UCLA wording
Requisites: courses 1A, 1B, 1C (or 1AH, 1BH, 1CH), Mathematics 32A, 33A, or equivalent. Preparation: some experience in programming using Python.
BruinTree reads · Prerequisite
needs reviewconfidence 0.00 · from text- equivalent
- · parenthetical alternative "(or 1AH, 1BH, 1CH)" in "1C (or 1AH, 1BH, 1CH)" — scope is a best reading
- · could not read "equivalent" (no course number found)
- · mixed comma-level AND/OR in "courses 1A, 1B, 1C (or 1AH, 1BH, 1CH), Mathematics 32A, 33A, or equivalent" — grouping is a best reading
BruinTree reads · Preparation
needs reviewconfidence 0.00 · from textBruinTree could not read this requirement — see UCLA’s wording above.
- · could not read "some experience in programming using Python" (no course number found)
- · no course reference could be read from this requisite
Requires
Everything that has to come before this course, not just the courses named in the requisite above.
PHYSICS C270M
- PHYSICS 1APhysics for Scientists and Engineers: Mechanics
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.