MATH 156Machine Learning
Mathematics · 4 units · Undergraduate upper division (100-199)
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.
When it runs
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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 textBruinTree reads · Recommended
confidence 1.00 · from textRequires
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 164Optimization
- COM SCI 31Introduction to Computer Science I
- MATH 170AProbability Theory I
- MATH 32BCalculus of Several Variables
- MATH 170EIntroduction to Probability and Statistics 1: Probability
- COMPTNG 10AIntroduction to Programming
- STATS 21Python and Other Technologies for Data Science
- COMPTNG 16APython with Applications I
- STATS 100AIntroduction to Probability
9 direct requisites. Showing 34 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.
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.