Electrical Engineering 126 — Probability and Random Processes (4 Units)

Note: EE126 is now EECS126.

Course Overview

Summary

This course first reviews basic probability concepts taught in CS70/Math 55 then it introduces new concepts and theorems such as moment, Law of large numbers MMSE, LLSE, n-d Gaussian distribution, etc. At last, there will be an introduction to random process.

Prerequisites

  • CS 70 preferred but not required (although in our opinion, it is strongly preferred). Familiarity with linear algebra recommended.

Topics Covered

  • Probability basics definitions and counting
  • Bayes' rule
  • Estimation: MMSE, LLSE
  • Iterated Expectation
  • Gaussian / Joint Gaussian
  • Markov and Chebyshev Inequalities
  • Law of Large Numbers
  • Chernoff Bounds and Central Limit Theorem
  • Bernoulli and Poisson Processes
  • Markov Chains and their Stationary Distributions

Workload

Course Work

Weekly Problem set.

Time Commitment

3 hours lectures, 1 hour of discussion and 6-8 hours of problem sets per week. This is known to be a very high workload course though, and you should be prepared to spend more than 10 hours per week on the homework.

Choosing the Course

When to take

After taking CS70, most probably sophomore or junior

What next?

  • EE226A, if you have a strong interest, particularly in the theoretical aspects of the class.
  • Courses like 188 and 189 explore the applications of probability.

Usefulness for Research or Internships

This course alone cannot help you to find research or internship but this course introduces material fundamental to many areas of research, including signal processing, machine learning, control theory, communications, information theory, so you cannot avoid it.

Additional Comments/Tips

Start early, go to office hours and form a study group.

Last edited: Fall 2017