EE278: Probability and Statistical Inference

Announcements

• Welcome to EE278, Fall 2026! The first lecture is Tuesday, September 22.
• Office hours, course platforms, and assessment policies will be announced.

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

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

Teaching assistant: Andy Dong, dxa at stanford.edu

Tentative midterms: Thursday, October 8, 2026 (week 3), and 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.

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