Discrete Probability Concepts and Models

Course Description

Fundamental concepts and tools for the analysis of problems under uncertainty, focusing on structuring, model building, and analysis. Examples from legal, social, medical, financial, engineering, physical, and gaming problems. Topics include combinatorics, axioms of probability, probability trees, conditioning, discrete random variables, distributions, conditional independence, belief networks, expectation, and probability bounds. The course is fast-paced, but it has no calculus prerequisite.

Course Details

  • Grading Basis: Letter Grade
  • Intensive Studies: This course is offered as part of the Data Science Intensive. See the Intensive Studies page for more information on how to receive an official Document of Completion.

Syllabus Link

None available.
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