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MATH 170EIntroduction to Probability and Statistics 1: Probability

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

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Highly recommended: course 61 or 70. Not open to students with credit for course 170A, Electrical and Computer Engineering 131A, or Statistics 100A. Introduction to probability theory with emphasis on topics relevant to applications. Topics include discrete (binomial, Poisson, etc.) and continuous (exponential, gamma, chi-square, normal) distributions, bivariate distributions, distributions of functions of random variables (including moment generating functions and central limit theorem).

P/NP or letter grading.

When it runs

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Requisites

Official UCLA wording

Requisite: course 32B.

BruinTree reads · Prerequisite

confidence 1.00 · from UCLA’s structured data
MATH 32B

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Requires

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

MATH 170E

  • MATH 32BCalculus of Several Variables
    • MATH 31BIntegration and Infinite Series
      • MATH 31ADifferential and Integral Calculus1 more beneath
    • MATH 32ACalculus of Several Variables
      • MATH 31ADifferential and Integral Calculusanother path to it

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.

MATH 170E8 more beyond

  • MATH 156Machine Learning
    • MATH M148Experience of Data Science
    • STATS 147Data Technologies for Data Scientists
    • STATS M148Experience of Data Science
    • STATS C163Generative Data Scienceanother path to it
    • STATS 184Societal Impacts of Data
  • MATH 168Introduction to Networks
  • CH ENGR M148Introduction to Data Science
  • MATH 170SIntroduction to Probability and Statistics 2: Statistics1 more beyond
    • MATH 178BFoundations of Actuarial Mathematics: Additional Topics in Long-Term Actuarial Mathematics
      • MATH 178CFoundations of Actuarial Mathematics: Loss Models
    • ECON 103Introduction to Econometricsanother path to it
    • STATS 100CLinear Models10 more beyond
    • ECON 103LEconometrics Laboratoryanother path to it
    • STATS 102BIntroduction to Computation and Optimization for Statistics
    • STATS 102CIntroduction to Monte Carlo Methods
    • STATS 140XPCollaboration in Data Science1 more beyond
    • STATS 143Introduction to Research in Statistics
  • MATH 171Stochastic Processes
    • EPS SCI 205Inverse Theory and Data Interpretation
  • COM SCI M119Fundamentals of Embedded Networked Systems
  • MATH 174EMathematics of Finance for Mathematics/Economics Students
    • MATH 179Advanced Topic in Financial Mathematics
  • MATH 178AFoundations of Actuarial Mathematics: Life Insurance and Annuities
  • EC ENGR M119Fundamentals of Embedded Networked Systems
  • ECON 103Introduction to Econometrics21 more beyond
    • ECON 103LEconometrics Laboratoryanother path to it
    • ECON 104Data Science for Economists
      • ECON 104LData Science for Economists Laboratoryanother path to it
    • ECON 104LData Science for Economists Laboratory
    • ECON 109CAdvanced Sequence: Computational Economicsanother path to it
    • ECON 109DAdvanced Sequence: Econometric Theoryanother path to it
    • ECON 111Theories of Development3 more beyond
    • ECON 111LTheories of Development Laboratory
    • ECON 130Public Economics1 more beyond
  • STATS 100BIntroduction to Mathematical Statistics10 more beyond
    • MATH M148Experience of Data Scienceanother path to it
    • MATH 178BFoundations of Actuarial Mathematics: Additional Topics in Long-Term Actuarial Mathematicsanother path to it
    • BIOMATH M207ATheoretical Genetic Modeling
    • BIOSTAT 125The Science of Why: Causal Inference for Public Health
    • HUM GEN M207ATheoretical Genetic Modeling
    • ECON 103Introduction to Econometricsanother path to it
    • BIOSTAT 213Introduction to Computational Methods in Biostatistics
    • BIOSTAT M272Theoretical Genetic Modeling
  • COM SCI C121Probabilistic Models in Computational Genomics
  • EC ENGR M146Introduction to Machine Learning
    • EC ENGR C147ANeural Networks and Deep Learning1 more beyond
      • EC ENGR C147BNeural Networks and Deep Learning II
    • STATS C163Generative Data Science
    • EC ENGR C247ANeural Networks and Deep Learning1 more beyond
  • ECON 103LEconometrics Laboratory
    • ECON 103Introduction to Econometricsanother path to it
    • ECON 109CAdvanced Sequence: Computational Economics
    • ECON 109DAdvanced Sequence: Econometric Theory
    • ECON 109DLAdvanced Sequence: Econometric Theory Laboratory
    • ECON 109EAdvanced Sequence: Applied Empirical Economics
    • ECON 123Forecasting of Asset Prices and Portfolio Design
    • ECON 123LForecasting of Asset Prices and Portfolio Design Laboratory
    • ECON 144Economic Forecasting
  • COM SCI C122Algorithms in Computational Genomics

23 courses list this as a requisite. Showing 60 courses over 3 levels; the branches marked with a count carry on past it. Every course here opens its own tree.

  • Fall 2025
  • Winter 2026
  • Spring 2026
  • Summer 2026
  • Fall 2026
  • Winter 2027
  • Spring 2027

Scheduled, not typical — from UCLA’s Schedule of Classes, which publishes Fall 2025 through Spring 2027 and nothing before it.