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Course Details

Fall 2017-2018
ELE 382   Graded A-F, P/D/F, Audit

Statistical Signal Processing

Yuxin Chen

A wide spectrum of engineering applications require efficient procedures to describe, process, analyze, and infer the signals/data of interest, which are often accomplished by imposing proper statistical models on the objects under consideration. This course introduces the fundamental statistical principles and methods that play a central role in modern signal and information processing. Specific topics include random processes, linear regression and estimation, hypothesis testing and detection, and shrinkage methods.

Sample reading list:
Alan Oppenheim and George Verghese, Signals, Systems and Inference
Dimitri P. Bertsekas and John N. Tsitsiklis, Introduction to Probability (2nd Edition)

Reading/Writing assignments:
Weekly problem sets, advance reading

Mid Term Exam - 30%
Final Exam - 30%
Problem set(s) - 40%

Other Requirements:
Not Open to Freshmen.

Prerequisites and Restrictions:
Basic linear algebra, basic calculus, and knowledge of a programming language like MATLAB or Python to conduct simulation exercises. Students should have background in basic probability (a course such as ORF309 would be beneficial) and "Fourier transform".


Schedule/Classroom assignment:

Class numberSectionTimeDaysRoomEnrollmentStatus
22251 L01 1:30 pm - 2:50 pm M W   Friend Center   003   Enrolled:18 Limit:50