Theory of Probability

Course Description

Probability spaces as models for phenomena with statistical regularity. Discrete spaces (binomial, hypergeometric, Poisson). Continuous spaces (normal, exponential) and densities. Random variables, expectation, independence, conditional probability. Introduction to the laws of large numbers and central limit theorem. 

Course Details

  • Grading Basis: Letter Grade or Credit/No Credit
  • Unit-Range Information: The amount of workload is monitored by the instructor, and it will increase or decrease relative to the number of units undertaken. Students are expected to attend all class/discussion sessions and perform work in proportion to the number of units in which they have enrolled (students often take only the number of units they need to fulfill a degree requirement).
  • Intensive Studies: This course is offered as part of the Data Science Intensive and must be taken for 5 units. See the Intensive Studies page for more information on how to receive an official Document of Completion.

Prerequisites

MATH 52 and familiarity with infinite series, or equivalent

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