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 EE 178 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; dimensionality reduction and generalization in machine learning, minimum mean square error estimation and Kalman filtering.
3 units · Letter or Credit/No Credit
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 EE 178 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; dimensionality reduction and generalization in machine learning, minimum mean square error estimation and Kalman filtering.
Offered in Autumn 2026 at Stanford University.