MATH 277

Foundations of Machine Learning and Artificial Intelligence

Mathematics · 4 units · Graduate courses (200-299)

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Introduction to mathematical and theoretical aspects of machine learning and artificial intelligence. Topics include probability and high-dimensional statistics, neural networks, deep learning, artificial intelligence, modern architectures, approximation theory, optimization, and error estimates.

S/U or letter grading.

Requisites

Official UCLA wording
Requisites: courses 115A, 131A, or equivalent.

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Requires

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

MATH 277

MATH 115ALinear Algebra
- MATH 33ALinear Algebra and Applications
  - MATH 3BCalculus for Life Sciences Students1 more beneath
  - MATH 31BIntegration and Infinite Seriesanother path to it
  - MATH 32ACalculus of Several Variablesanother path to it
MATH 131AAnalysis
- MATH 32BCalculus of Several Variables
  - MATH 31BIntegration and Infinite Series1 more beneath
  - MATH 32ACalculus of Several Variables1 more beneath
- MATH 33BDifferential Equations
  - MATH 31BIntegration and Infinite Series

2 direct requisites. Showing 11 courses over 3 levels; the branches marked with a count carry on past it. Every course here opens its own tree.

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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.