EE 278: Course Information

Stanford University, Balaji Prabhakar, Autumn Quarter 2018-2019


You can find the course syllabus in pdf format here.

Course Outline

The following topics will be covered in the course:

  • Review of basic probability and random variables

  • Random vectors and processes

  • Convergence and limit theorems

  • IID, independent increment, Markov, and Gaussian random processes

  • Stationary random processes

  • Autocorrelation and power spectral density

  • Mean square error estimation, detection, and linear estimation.


Time: Tuesdays and Thursdays 12:00 PM-1:20 PM
Location: Thornt 102

Review Sessions

There will be a weekly review session, starting from the second week of the quarter.
Time: Tuesdays 4:30 PM-5:20 PM
Location: Huang 018

Office hours

Office hours will be held each week. Office hours are intended to be a time for discussion about general class topics, homework, and review exercises.

Instructor: Balaji Prabhakar
Office hours: Tuesdays 1:30-3:00PM
Packard 269

CA: Ahmad Ghalayini
Office hours: Thursday 10-11:30AM (for both on-campus and SCPD students)
Location: Packard 106


We will be using a class Piazza forum to conduct all discussions about course materials, answer questions about homework and review exercises, and post announcements outside of the scheduled lectures and office hours.
Please sign up here.

Course Requirements

Homework: There will be weekly homework sets.
Time: They will be made available on Thursdays after class and are due on the following Thursday by 11:59 pm on Gradescope. Gradescope code: 9PX2N5

Midterm: It will be a 4 hour take-home exam, and must be submitted to Gradescope.
Time: Tuesday October 30, 2-6 PM.

Final: It will be a 24 hour take-home exam, and must be submitted to Gradescope.
Time: 2 PM on Thursday December 6 to 2 PM on Friday December 7.

Grading guideline: 20% Homework, 35% Midterm, 45% Final

Course Prerequisites

EE 178 and linear systems and Fourier transforms at the level of EE 102A,B or EE 261, basic linear algebra, and basic knowledge of a language like MATLAB or Python to do some simulation exercises.