MATH 156

Machine Learning

Mathematics · 4 units · Undergraduate upper division (100-199)

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Introductory course on mathematical models for pattern recognition and machine learning. Topics include parametric and nonparametric probability distributions, curse of dimensionality, correlation analysis and dimensionality reduction, and concepts of decision theory. Advanced machine learning and pattern recognition problems, including data classification and clustering, regression, kernel methods, artificial neural networks, hidden Markov models, and Markov random fields. Projects in MATLAB to be part of final project presented in class.

P/NP or letter grading.

Requisites

Official UCLA wording
Requisites: courses 115A, 164, 170A or 170E or Statistics 100A, and Computer Science 31 or Program in Computing 10A. Strongly recommended requisite: Program in Computing 16A or Statistics 21.

BruinTree reads · Prerequisite

confidence 1.00 · from text

all of

BruinTree reads · Recommended

confidence 1.00 · from text

one of

Requires

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

MATH 156

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

Unlocks

What this course is a requisite for, and what those courses lead to in turn.

MATH 156

MATH M148Experience of Data Science
STATS 147Data Technologies for Data Scientists
STATS M148Experience of Data Science
STATS C163Generative Data Science
STATS 184Societal Impacts of Data

5 courses list this as a requisite. The whole downstream is here — 5 courses over 1 level. Every course here opens its own tree.