M.SC. in Computer Engineering (Without Thesis)

EEE 652 | Course Introduction and Application Information

Course Name

Stochastic Processes

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
EEE 652	Fall/Spring	3	0	3	7.5

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

Course Objectives

The course aims to study basic probability concepts, time averages, statistical averages, random variables, moments, CDF, PDF, and PSD functions, random vectors, stationary and ergodic random processes and random signals

Learning Outcomes

#	Content	PC Sub	* Contribution Level
#	Content	PC Sub	1	2	3	4	5
1	be able to determine the probability density function and calculate probability,
2	be able to identify the parameters of discrete and continuous random variables,
3	be able to determine the characteristics of the sum of the random variables,
4	be able to do hyphothesis testing based on the random observations,
5	be able to determine the autocorrelation and power spectral density of random signals,
6	be able to obtain the best estimate based on the random measurements,
7	be able to determine the Markov Chain model of random processes,

Course Description

This is a mandatory course in Electrical and Electronics Engineering Ph.D. Program. The course is in the area of Random Signals, Probability Theory, Stochastic Processes and Random Signal Processing. The course aims to study basic probability concepts, time averages, statistical averages, random variables, moments, CDF, PDF, and PSD functions, random vectors, stationary and ergodic random processes and random signals

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	Review of signals and systems	Textbooks Ch 1
2	Time averages, correlation, covariance	Textbooks Ch 7
3	Processing of random signals by LTI systems	Textbooks Ch 7
4	Applications: Estimation problem	Textbooks Ch 7
5	Probability theory	Textbooks Ch 2
6	Random variable concept	Textbooks Ch 3
7	Cumulative Distribution and Probability Density Functions	Textbooks Ch 3
8	Statistical averages, mean, variance, higher order moments	Textbooks Ch 3
9	Random vectors	Textbooks Ch 4
10	Functions of random variables	Textbooks Ch 5
11	Characteristic function of random variables	Textbooks Ch 5
12	Stochastic processes	Textbooks Ch 6
13	Ergodic and stationary processes	Textbooks Ch 6
14	Applications in Electrical and Electronics Engineering	Textbooks Ch 7
15	Review of the course and problem session
16	Final

Course Notes/Textbooks	Alberto Leon-Garcia, Probability, Statistics, and Random Processes for Electrical Engineering, 3/E, Pearson, 2008. ISBN: 9780131471221
Suggested Readings/Materials	Stark and Woods, Probability and Random Processes with Applications to Signal Processing, 4/E Pearson, 2012 ISBN: 9780273752288

EVALUATION SYSTEM

Semester Activities	Number	Weighting	LO 1	LO 2	LO 3	LO 4	LO 5	LO 6	LO 7
Participation
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments	6	30
Presentation / Jury
Project
Seminar / Workshop
Oral Exams
Midterm	1	30
Final Exam	1	40
Total

Weighting of Semester Activities on the Final Grade	7	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	14	5	70
Field Work			0
Quizzes / Studio Critiques			0
Portfolio			0
Homework / Assignments	5	11	55
Presentation / Jury			0
Project			0
Seminar / Workshop			0
Oral Exam			0
Midterms	1	20	20
Final Exam	1	32	32
		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	-	-	-