Contact

Please contact us by reaching on the staff email list: ee274-aut2324-staff@lists.stanford.edu, 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

  • Pulkit Tandon
    • Research Engineer at Granica
    • tpulkit [AT] stanford [DOT] edu
    • Office hours: Monday, 6pm, Shriram 104
  • Shubham Chandak
    • Sr. Applied Scientist at S3, Amazon Web Services
    • schandak [AT] stanford [DOT] edu
    • Office hours: Wednesday, 6pm, Shriram 104
  • Prof. Tsachy Weissman
    • Electrical Engineering, Stanford
    • tsachy [AT] stanford [DOT] edu
    • Office hours: Wednesday, 3-4pm, Packard Room No. 256
  • Noah Huffman
    • Physics, Stanford
    • nhuffman [AT] stanford [DOT] edu
    • Office hours: Tuesday, 1:30pm-2:30pm, Packard Room No. 318

Lectures

Disclaimer

Video cameras located in the back of the room will capture the instructor presentations in this course. For your convenience, you can access these recordings by logging into the course Canvas site. 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 (tentative)

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 after every lecture, due before next (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.

  • Stanford Compression Library (a collection of compression algorithms implemented in Python)
  • Lecture Notes (for the course notes)
  • ED: available via Canvas (for course Q&A, discussions and announcements)
  • Gradescope: available via Canvas (for quizzes and assignment submissions)
  • Panopto: available via Canvas (for the course lectures video recordings)
  • IT Forum (for talks on various topics related to compression)

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.