Electrical Engineering 126 — Probability and Random Processes (4 Units)
Note: EE126 is now EECS126.
This course first reviews some basic probability concepts taught in CS70 before moving on to new concepts and theorems such as Moment Generating Functions, Law of Large Numbers, Estimation Theory, and n-dimensional Gaussian distributions. It also provides an introduction to random processes—notably Markov Chains, Continuous-Time Markov Chains, and the Poisson Process.
- CS 70 or other intro probability theory
- Familiarity with linear algebra
- 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
Weekly Problem set. Semi-regular weekly Python labs.
3 hours lectures, 1 hour of discussion, 6-8 hours of problem sets per week, and some weeks will have 2-4 hours of Python labs [the main challenge with labs comes from debugging and decoding ambiguous directions]. 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
- EE226A, if you have a strong interest, particularly in the theoretical aspects of the class.
- EECS229A covering information theory.
- Courses like 188 (less dependent on this course) and 189 explore the applications of probability.
Usefulness for Research or Internships
This course is fundamental to many areas of research, including signal processing, machine learning, control theory, communications, information theory. It is unlikely to be useful for internships, but a good grade in 126 can serve as a nice feather in your cap while trying to find research.
Start early, go to office hours and form a study group. Form a study group. Form a study group. Also, form a study group.
Last edited: Summer 2020