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STATS 100A

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Introduction to Probability

Requisites: Mathematics 32B, 33A.

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34 courses across 5 departments, 15 choice points · 26 requisite links run off the edge of this view

STATS 100A — Introduction to ProbabilitySTATS 100AIntroduction to Probab…MATH 32B — Calculus of Several VariablesMATH 32BCalculus of Several Va…MATH 31B — Integration and Infinite SeriesMATH 31BIntegration and Infini…MATH 31A — Differential and Integral CalculusMATH 31ADifferential and Integ…MATH 32A — Calculus of Several VariablesMATH 32ACalculus of Several Va…MATH 33A — Linear Algebra and ApplicationsMATH 33ALinear Algebra and App…MATH 3B — Calculus for Life Sciences StudentsMATH 3BCalculus for Life Scie…MATH 3A — Calculus for Life Sciences StudentsMATH 3ACalculus for Life Scie…STATS 100B — Introduction to Mathematical StatisticsSTATS 100BIntroduction to Mathem…STATS 100C — Linear ModelsSTATS 100CLinear ModelsSTATS C151 — Experimental DesignSTATS C151Experimental DesignSTATS 170 — Introduction to Time-Series AnalysisSTATS 170Introduction to Time-S…BIOMATH M257 — Computational Methods for Biostatistical ResearchBIOMATH M257Computational Methods…STATS C173 — Applied GeostatisticsSTATS C173Applied GeostatisticsSTATS M230 — Statistical ComputingSTATS M230Statistical ComputingSTATS 102B — Introduction to Computation and Optimization for StatisticsSTATS 102BIntroduction to Comput…BIOSTAT 125 — The Science of Why: Causal Inference for Public HealthBIOSTAT 125The Science of Why: Ca…STATS 102C — Introduction to Monte Carlo MethodsSTATS 102CIntroduction to Monte…STATS C116 — Applied Bayesian Social StatisticsSTATS C116Applied Bayesian Socia…STATS 105 — Statistics for EngineersSTATS 105Statistics for Enginee…BIOINFO M223 — Statistical Methods in Computational BiologyBIOINFO M223Statistical Methods in…STATS 115 — Probabilistic Decision MakingSTATS 115Probabilistic Decision…BIOMATH M271 — Statistical Methods in Computational BiologyBIOMATH M271Statistical Methods in…STATS C160 — Causal Inference for Health DataSTATS C160Causal Inference for H…STATS 200A — Applied ProbabilitySTATS 200AApplied ProbabilitySTATS 201B — Statistical Modeling and LearningSTATS 201BStatistical Modeling a…STATS 206 — Modern Survey MethodsSTATS 206Modern Survey MethodsSTATS 211 — Topics in Economics and Machine LearningSTATS 211Topics in Economics an…STATS 213 — Synthetic Data GenerationSTATS 213Synthetic Data Generat…STATS C263 — Generative Data ScienceSTATS C263Generative Data ScienceSTATS 219 — Topics in Reinforcement LearningSTATS 219Topics in Reinforcemen…BIOSTAT 276 — Inferential Techniques that Use SimulationBIOSTAT 276Inferential Techniques…STATS 231C — Theories of Machine LearningSTATS 231CTheories of Machine Le…STATS 238 — Vision as Bayesian InferenceSTATS 238Vision as Bayesian Inf…Also satisfied by BIOINFO M221, which UCLA names as a requisite but does not publish in this catalog.one of +1Also satisfied by BIOSTAT 250A, not drawn in this view.one of +1one ofone ofAlso satisfied by MATH 170A, MATH 170E, not drawn in this view.one of +2Also satisfied by MATH 170S, not drawn in this view.one of +1Also satisfied by MATH 170S, not drawn in this view.one of +1Also satisfied by MATH 170S, not drawn in this view.one of +1Also satisfied by EC ENGR 131A, MATH 170A, not drawn in this view.one of +2Also satisfied by STATS 101A, not drawn in this view.one of +1Also satisfied by MATH 170A, not drawn in this view.one of +1Also satisfied by STATS 200B, STATS 202A, STATS M231A, STATS 231B, not drawn in this view.one of +4one ofAlso satisfied by STATS 101B, not drawn in this view.one of +1Also satisfied by STATS 200B, STATS 202A, STATS M231A, STATS 231B, not drawn in this view.one of +4
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