EE278: Probability and Statistical Inference

Ayfer Ozgur, Stanford University, Autumn 2023

Course Description

Many engineering applications require efficient methods to process, analyze, and infer signals, data and models of interest that are best described probabilistically. Building on a first course in probability (such as EE178 or equivalent), this course introduces more advanced topics in probability such as concentration inequalities, random vectors and random processes, and explores their applications in statistics, machine learning and signal processing. Specific applications include hypothesis testing and classification; principal component analysis and generalization in machine learning, minimum mean square error estimation and Kalman filtering.

Lectures (also recorded through SCPD)

Time: Tue, Thu 1:30 - 2:50 pm

Location: Thornton 102

Problem sessions

Time: TBD

Location: TBD