A&O SCI C204

Introduction to Machine Learning for Physical Sciences

Atmospheric and Oceanic Sciences · 4 units · Graduate courses (200-299)

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Lecture, 90 minutes; laboratory, 90 minutes. Designed for physical sciences students. Practical, hands-on introduction to seven of most popular algorithms of machine learning (ML). Students gain most practical skills to start working in industry or research immediately, using popular Python programming language, together with SciKitLearn ML library, and covering essential theory to understand what algorithms do. Focus on solving typical problems that arise in physical sciences. Covers algorithms in broad areas of ML, including supervised learning (regression and classification) and unsupervised learning (clustering and dimensionality reduction). Lectures and programming exercises. Concurrently scheduled with course C111.

S/U or 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.

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Requisites

Official UCLA wording
Requisite: Mathematics 115A or equivalent course in linear algebra.

BruinTree reads · Prerequisite

confidence 1.00 · from UCLA’s structured data

MATH 115A

Requires

Everything that has to come before this course, not just the courses named in the requisite above.

A&O SCI C204

MATH 115ALinear Algebra
- MATH 33ALinear Algebra and Applications
  - MATH 3BCalculus for Life Sciences Students1 more beneath
  - MATH 31BIntegration and Infinite Series1 more beneath
  - MATH 32ACalculus of Several Variables1 more beneath

1 direct requisite. Showing 5 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.