EE 278: Introduction to Statistical Signal Processing

Instructor: Professor Balaji Prabhakar
Course Assistant: Pin Pin Tea-mangkornpan
Contact: balaji, pinnaree at stanford


  • Dec 5: Announcements related to the final are posted here (on Piazza).

  • Nov 30: Sample finals are posted below under Exams section.

  • Nov 16: Homework 7 is posted here and will be due Tuesday 11/27 at 4 p.m. Homework 8 will be out after class on Tuesday 11/27 and due the following week Wednesday 12/6 at 4 p.m.

  • Nov 7: Midterms are graded and available for pick-up with Kara Marquez at Packard 267 today from 1.30 to 4 p.m. Please see the details here.

  • Oct 31: Lecture notes on neural networks and The New York Times article are posted here.

  • Oct 21: Sample midterms are posted below under Exams section.

  • Oct 20: Pin Pin's office hours is moved from Monday to Tuesday 2:30 PM to 3:30 PM. See details below.

  • Oct 17: Announcements related to the midterm are posted here (on Piazza).

  • Oct 12: Please fill in this form to sign up for your group (3-4 members per group). Please submit only 1 form per group. If your group has less than 3 people at the moment, please provide the names of current members in the form and we will find a group for you. Piazza has a Search for Teammates tool that you can leverage of.

  • Oct 5: We will use Canvas to support code submission. Please upload your file in Homework 1, under the Assignments tab.

  • Sep 28: Homework 1 is posted here, and previous EE 178 and EE 278 lecture notes are posted here.

  • Sep 27: The review sessions are going to be held at 380-380C starting Wednesday Oct 4.

Course Information

Prerequisite: EE178 and linear systems and Fourier transforms at the level of EE102A,B or EE261.

Materials: 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. Formerly EE 278B.

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


Midterm: Take home. Out Thursday October 26 at 2:00 PM (after class) and due Friday October 27 at 2:00 PM.

Final: Take home. Out Thursday December 7 at 2:00 PM (after class) and due Friday December 8 at 2:00 PM.


Tuesdays and Thursdays, 12:00 PM - 1:20 PM at 420-041

Lecture notes will be posted after class on Materials page.

Office Hours:
Professor Prabhakar, Wednesdays 10:00 AM - 11:00 AM and Thursdays 9:30 AM - 10:30 AM at Packard 269
Pin Pin Tea-mangkornpan, Tuesdays 2:30 PM - 3:30 PM at Packard 104

Review Session:
Wednesdays 4:30 PM - 5:30 PM at 380-380C

Piazza forum

Please sign up at