Course Information


Probability is a mathematical discipline for reasoning about randomness: it helps us make decisions in the face of uncertainty and build better systems. In this course, we will teach you the fundamental ideas of probability and random processes. The various assignments are carefully designed to strengthen your mathematical understanding of probability and to demonstrate how these concepts can be applied to the real world, be it in communication networks, control systems, or machine learning.


Knowledge of probability at the level of CS 70 or STAT 134. Linear algebra at the level of EECS 16A or Math 54.

Course Outline

  1. Fundamentals of Probability / 4 weeks
    • Review: Discrete and Continuous Probability
    • Bounds, Convergence of Random Variables, Law of Large Numbers
    • Discrete Time Markov Chains
  2. Random Processes and Estimation / 6 weeks
    • Transforms, Central Limit Theorem
    • Queueing, Poisson Processes, Continuous Time Markov Chains
    • Communication, Information Theory
    • MLE/MAP, Detection, Hypothesis Testing
  3. Applications of Probability / 4 weeks
    • Kalman Filtering, Tracking
    • Markov Decision Problems, Linear Quadratic Gaussian Control
    • Hidden Markov Chains, Optimization


The course will follow the new Walrand textbook (see Piazza for access).

Other References

Some students may find it helpful to reference parts of the B&T textbook, but we will not be using it this semester, and it is not necessary.


We will be using Piazza for class discussion. Rather than emailing questions to the GSIs, we encourage you to post your questions on Piazza.


The grading breakdown is as follows:


We will be using a clobber policy where your final can replace your grade for either MT1 or MT2, but not both.

Exams will be held during lecture times (Pacific Time):

See the exams page for details.



Self-Grading Policy

We will periodically be checking self-grades internally to ensure that they are accurate. If we find that your self-grades do not align with our scores (either positively or negatively), we will reach out to you and adjust your self-grades. If you do not hear from us, your self-grades will be used for your homework grade.


Discussions about assignments are allowed and encouraged, but each student is expected to write his/her own solutions.