EE178: Probabilistic Systems Analysis

David Tse ( Autumn 2016-2017

Course Description

The goal of the course is to introduce probabilistic modeling and its role in solving engineering problems. The course is divided into two parts. The first part introduces the basic concepts of probability: random variables , sample space, events, probability, conditional probability, independence, probability mass functions and density functions, expectation, law of large numbers. Using the language acquired in the first part, the second part discusses several applications chosen from data storage, ranking of webpages, network multiplexing, digital communication, positioning, speech recognition and computational biology. This is a course about “probability in action”: probabilistic concepts are taught through many non-trivial examples, engineering applications and Python labs.


  • Mon, Wed, Fri 10:30 AM - 11:20 AM at Sequoia Hall 200


Additional Office Hours

  • Friday Dec 9, 2:30pm - 3:30pm David (264 Packard)

  • Mon Dec 12, 3pm - 5pm David and Cheuk Ting (3rd floor Packard kitchen)

  • Tues Dec 13, 11am - noon David (264 Packard)

  • Wed Dec 14, 3pm - 5pm Cheuk Ting (3rd floor Packard kitchen)