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. Linear algebra at the level of EE 16A or Math 54.


Course Outline

  1. Fundamentals of Probability
    • Discrete and Continuous Probability
    • Order Statistics, Convolution, and Moment Generating Functions
    • Bounds, Convergence, and Information Theory
  2. Random Processes
    • Discrete and Continous Time Markov Chains
    • Poisson Processes
    • Erdos-Renyi Random Graphs
  3. Inference and Estimation
    • MLE/MAP and Hypothesis Testing
    • LLSE/MMSE, Kalman Filter, and Jointly Gaussian Random Variables
    • Fisher Information and Cramer-Rao Bound


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


The grading breakdown is as follows:


For the exam grade, MT1 and MT2 will make up 25% each and the final will make up 50%. This semester, we will be using a clobber policy where your final can replace your grade for either MT1 or MT2 but not both.

For example, if a student gets full credit on Homework and Labs, then receives a 73 on MT1, 34 on MT2, and a 80 on the Final, their grade would be computed as follows: Final Grade = .15 + .10 + .75 x (.73 x .25 + .80 x .25 + .80 x .50).

The exams will be at the following times, it is your responsibility to notify us ASAP if you cannot make them.

See the exams page for more details.




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

Self Grades