A&O SCI C111
Introduction to Machine Learning for Physical Sciences
Atmospheric and Oceanic Sciences · 4 units · Undergraduate upper division (100-199)
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 C204.
P/NP or letter grading.
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Requisite: Mathematics 115A or equivalent course in linear algebra.
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A&O SCI C111
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