SYS 6045

Applied Probabilistic Models

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Course Description

The goal of this course is to develop an operational understanding of the basic tools of probabilistic modeling, including (i) a review of undergraduate probability, (ii) introduction to Bernoulli and Poisson processes with applications, (iii) Markov chains and applications, and (iv) limit theorems. Homework and exams will emphasize the use of basic concepts of probability theory in applications. This course cannot be applied toward completing the requirements for an M.S. or Ph.D. in Systems Engineering.


  • Stephen Patek

     Rating

     Difficulty

     GPA

    3.45

     Sections

    Last Taught

    Summer 2021

  • William Scherer

     Rating

     Difficulty

     GPA

     Sections

    Last Taught

    Summer 2021

  • Michael Smith

     Rating

     Difficulty

     GPA

     Sections

    Last Taught

    Summer 2017

  • Garrick Scherer

     Rating

     Difficulty

     GPA

     Sections

    Last Taught

    Summer 2019