EE278: Probability and Statistical Inference

Stanford University, Ayfer Özgür and Tsachy Weissman, Fall 2026

Announcements

Welcome to EE278, Fall 2026! The first lecture is Tuesday, September 22.
Information about grading, weekly quizzes, and lecture scribing is available on the Course Info page.
Lecture scribing assignments are posted on Ed. Share your notes as a PDF in an Ed post visible to the whole class within four calendar days of your lecture, by 11:59 pm Pacific time. See the scribing instructions.
If you are not automatically enrolled, use Gradescope entry code EBVYPR or the Ed join link to join the corresponding course platform.
See Course Info for office hours.

Course Overview

Many engineering problems involve making inferences from noisy, uncertain, or incomplete data. This course develops fundamental approaches to statistical inference from both model-based and data-driven perspectives, emphasizing their connections and differences. Topics include hypothesis testing and classification; empirical risk minimization and generalization; minimum mean-square error and linear estimation; random vectors, Gaussian models, and principal component analysis; Kalman filtering; random processes and power spectral density; and Wiener filtering and prediction. Throughout the course, we use probability as a common language for understanding classical signal processing methods and learning-based approaches to inference.

Lectures

Time: Tuesdays and Thursdays, 12:00–1:20 pm (Pacific time)

Location: Gates B3

Format: In person

Recordings: Lectures are recorded and available in Canvas under Panopto Course Videos.

Term: September 22–December 4, 2026 (2026–2027 Autumn)

Midterms: Midterm I on Tuesday, October 13, 2026 (week 4), and Midterm II on Thursday, November 5, 2026 (week 7). Exam arrangements will be announced.

Final exam: Friday, December 11, 2026, 12:15–3:15 pm (Pacific time). Location to be announced.

See Course Info for registration and administrative details.

Web Accessibility

Stanford University is committed to providing an online environment that is accessible to everyone, including individuals with disabilities.

If you experience difficulty accessing course content on this website, please reach out to the course staff and use Stanford’s accessibility resources to report the issue.