Syllabus Application
EE 568
Detection and Estimation Theory
Faculty
Faculty of Engineering and Natural Sciences
Semester
Fall 2026-2027
Course
EE 568 -
Detection and Estimation Theory
Time/Place
Time
Week Day
Place
Date
09:40-11:30
Tue
FENS-L047
Sep 28-Dec 31, 2026
12:40-13:30
Thu
FENS-L056
Sep 28-Dec 31, 2026
Level of course
Masters
Course Credits
SU Credit:3, ECTS:10
Prerequisites
EE 550 and EE 550
Corequisites
-
Course Type
Lecture
Instructor(s) Information
Mohaned Chariti
Course Information
Catalog Course Description
Principle of estimation, detection and time series analysis. Estimation: Linear and nonlinear minimum mean squared error ,estimation and other strategies. Detection: simple, composite, binary and multiple hypotheses, Neyman-Pearson and Bayesian approaches. Time series analysis: Wiener, Kalman filtering , prediction and modal Analysis.
Learning Outcomes:
| 1. |
|---|
Course Objective
Major objectives of this course are 1) to provide the statistical foundations of hypothesis testing, signal detection, and parameter estimation; 2) to derive and analyze optimum decision and estimation techniques under different statistical models; 3) to understand when model-based inference is sufficient and when data-driven machine-learning techniques become useful; and 4) to build reproducible comparisons and benchmarks for detection and estimation methods.
-
Course Materials
Resources:
An Introduction to Signal Detection and Estimation, H. Vincent Poor Second Edition, Springer, 2013.
Machine Learning: A Bayesian and Optimization Perspective, S. Theodoridis, Second Edition, Academic Press, 2020.
Machine Learning: A Bayesian and Optimization Perspective, S. Theodoridis, Second Edition, Academic Press, 2020.