Contact

Please contact us by reaching on each staff’s email or by making a private post on ED for the registered students. Individual appointments outside OH can be made by directly contacting the respective staff.

Course Staff

  • Prof. Tsachy Weissman (Instructor)
    • Electrical Engineering, Stanford
    • tsachy [AT] stanford [DOT] edu
    • Office hours: Tuesday, 12-1pm, Packard 256
  • Shubham Chandak (Instructor)
    • Sr. Applied Scientist at Annapurna, Amazon Web Services
    • schandak [AT] stanford [DOT] edu
    • Office hours: Monday, 8-9pm, Online (Link on Ed)
  • Kedar Tatwawadi (Guest Lecturer)
    • ML/Video Researcher at Apple
    • kedart [AT] stanford [DOT] edu
    • Office hours: Tuesday, 3-4pm, Thornton 208
  • Pulkit Tandon (Guest Lecturer)
    • Research Engineer at Granica
    • tpulkit [AT] stanford [DOT] edu
    • Office hours: Tuesday, 3-4pm, Thornton 208
  • Jiwon Jeong (TA)
    • Electrical Engineering, Stanford
    • jeongjw [AT] stanford [DOT] edu
    • Office hours: Thursday, 10-11am, Packard 104

Lectures

Disclaimer

We will be recording some of these sessions. These recordings might be reused in other Stanford courses, viewed by other Stanford students, faculty, or staff, or used for other education and research purposes. Note that while the cameras are positioned with the intention of recording only the instructor, occasionally a part of your image or voice might be incidentally captured. If you have questions, please contact a member of the teaching team.

Course elements and grading

EE 274 is a 3 unit course - auditing allowed with instructor permission. The graded course elements include:

  • 4 assignments with both theoretical and programming components (15% each)
  • Short quizzes every week, due before next in-person lecture (10%)
  • final project (30%)
  • [bonus] participation in the course (5%)

Audit Policy

Audits are more than welcome! All the material is released via website. Please contact instructors via the staff mailing list to get access to quizzes and HW submissions. For Stanford students taking the course for CR/NC, we do require that you get 50% of the total grade to get a CR.

Prerequisites

Basic probability and programming background (EE178, CS106B or equivalent), or instructor’s permission. Background in statistical signal processing (EE278) and in information theory (EE276) may be helpful for appreciating some of the material, but is not assumed and the relevant background will be covered in class. Some of the final projects will be tailored to the students’ backgrounds.

Reading Material

There is no required textbook. Lecture notes and slides will be provided as relevant. Working notes draft can be found here. We might also provide references to textbooks from time to time for additional reading. For a handy list of resources, you can also check out the resources page.

Honor Code

When in doubt follow the detailed Honor Code: https://communitystandards.stanford.edu/policies-guidance/honor-code

Thumb rules

  • You are permitted to seek assistance from peers, online resources, and your favorite LLM, etc.; however, it is imperative that you submit your own original work and acknowledge all the external resources consulted.
  • If ever in doubt, reach out to us.
  • We will regretfully, but strictly, follow honor code guidelines in case of any violations.