M.SC. in Computer Engineering (Without Thesis)

EEE 542 | Course Introduction and Application Information

Course Name

Detection and Estimation Theory

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
EEE 542	Fall/Spring	3	0	3	7.5

Prerequisites	None
Course Language	English
Course Type	Elective
Course Level	Second Cycle
Mode of Delivery	-
Teaching Methods and Techniques of the Course	-
National Occupation Classification	-
Course Coordinator	-
Course Lecturer(s)	Prof. Dr. Murat AŞKAR
Assistant(s)	-

Course Objectives

This course aims to provide a graduate-level introduction to detection and estimation theory. Course content includes the topics such as Gauss-Markov processes and stochastic differential equations, Bayes estimation theory, maximum likelihood, linear minimum deviation, minimum-squares estimation, properties of estimators, error analysis, state prediction for linear systems, Kalman-Bucy and Wiener filters, leveling and pre-estimation methods, nonlinear estimation, filtering applications, communications, control, system identification and biomedical engineering applications.

Learning Outcomes

#	Content	PC Sub	* Contribution Level
#	Content	PC Sub	1	2	3	4	5
1	learn how to use and discuss the maximum likelihood, maximum a posteriori probability and least-squares estimates of a parameter,
2	learn how to perform Karhunen-Loeve expansion,
3	learn how to apply Wiener filter and Kalman filter to solve linear estimation problems,
4	be able to evaluate performance of decision making and estimation systems,
5	be able to design and implement various detection and estimation algorithms using Matlab simulation software.

Course Description

Gauss-Markov processes and stochastic differential equations, Bayes estimation theory, maximum likelihood, linear minimum deviation, minimum-squares estimation, properties of estimators, error analysis, state prediction for linear systems, Kalman-Bucy and Wiener filters, leveling and pre-estimation methods, nonlinear estimation, filtering applications, communications, control, system identification and biomedical engineering applications.

Course Category	Core Courses
	Major Area Courses
	Supportive Courses
	Media and Management Skills Courses
	Transferable Skill Courses

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week	Subjects	Related Preparation	Learning Outcome
1	Introduction, Probability, Random Vectors, Vector Spaces	Lecture notes
2	Detection Theory, Decision Theory, and Hypothesis Testing	Lecture notes
3	Detection Theory, Decision Theory, and Hypothesis Testing	Lecture notes
4	Detection Theory, Decision Theory, and Hypothesis Testing	Lecture notes
5	Parameter Estimation	Lecture notes
6	Maximum Likelihood Estimation	Lecture notes
7	Stochastic Processes and Systems	Lecture notes
8	Midterm
9	Karhunen-Loeve and Sampled Signal Expansions	Lecture notes
10	Detection and Estimation from Waveform Observations	Lecture notes
11	Wiener and Kalman Filtering	Lecture notes
12	Advanced Topics	Lecture notes
13	In-class Presentations
14	In-class Presentations
15	In-class Presentations
16	Review of the Semester

Course Notes/Textbooks	Fundamentals of Statistical Signal Processing, Volume 1: Estimation Theory, Steven Kay, 1993. ISBN 0-13-345711-7 Fundamentals of Statistical Signal Processing, Volume 2: Detection Theory, Steven Kay, 1998. ISBN 0-13-504135-X
Suggested Readings/Materials	Related Research Papers

EVALUATION SYSTEM

Semester Activities	Number	Weighting	LO 1	LO 2	LO 3	LO 4	LO 5
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments	5	30
Presentation / Jury
Project	1	30
Seminar / Workshop
Oral Exams
Midterm
Final Exam	1	40
Total

Weighting of Semester Activities on the Final Grade	6	60
Weighting of End-of-Semester Activities on the Final Grade	1	40
Total

ECTS / WORKLOAD TABLE

Semester Activities	Number	Duration (Hours)	Workload
Theoretical Course Hours (Including exam week: 16 x total hours)	16	3	48
Laboratory / Application Hours (Including exam week: '.16.' x total hours)	16		0
Study Hours Out of Class	15	4	60
Field Work			0
Quizzes / Studio Critiques			0
Portfolio			0
Homework / Assignments	5	10	50
Presentation / Jury			0
Project	1	45	45
Seminar / Workshop			0
Oral Exam			0
Midterms			0
Final Exam	1	22	22
		Total	225

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#	PC Sub	Program Competencies/Outcomes	* Contribution Level
#	PC Sub	Program Competencies/Outcomes	1	2	3	4	5
1	Accesses information in breadth and depth by conducting scientific research in Computer Engineering, evaluates, interprets and applies information.		-	-	-	X	-
2	Is well-informed about contemporary techniques and methods used in Computer Engineering and their limitations.		-	-	X	-	-
3	Uses scientific methods to complete and apply information from uncertain, limited or incomplete data, can combine and use information from different disciplines.		-	-	-	X	-
4	Is informed about new and upcoming applications in the field and learns them whenever necessary.		-	-	-	-	X
5	Defines and formulates problems related to Computer Engineering, develops methods to solve them and uses progressive methods in solutions.		-	-	-	-	X
6	Develops novel and/or original methods, designs complex systems or processes and develops progressive/alternative solutions in designs.		-	-	-	X	-
7	Designs and implements studies based on theory, experiments and modelling, analyses and resolves the complex problems that arise in this process.		-	-	-	X	-
8	Can work effectively in interdisciplinary teams as well as teams of the same discipline, can lead such teams and can develop approaches for resolving complex situations, can work independently and takes responsibility.		-	-	X	-	-
9	Engages in written and oral communication at least in Level B2 of the European Language Portfolio Global Scale.		-	-	X	-	-
10	Communicates the process and the results of his/her studies in national and international venues systematically, clearly and in written or oral form.		-	-	X	-	-
11	Is knowledgeable about the social, environmental, health, security and law implications of Computer Engineering applications, knows their project management and business applications, and is aware of their limitations in Computer Engineering applications.		-	-	X	-	-
12	Highly regards scientific and ethical values in data collection, interpretation, communication and in every professional activity.		-	X	-	-	-