M.SC. in Computer Engineering (Without Thesis)

IE 540 | Course Introduction and Application Information

Course Name

Applied Stochastic Processes

Code	Semester	Theory (hour/week)	Application/Lab (hour/week)	Local Credits	ECTS
IE 540	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)	-
Assistant(s)	-

Course Objectives

The emphasis of the course will be on the development of tools that are useful in the analysis of stochastic systems that appear in real life. The course will start with the introduction to probability theory, distributions, and expectations and then continue with poisson process and Markov chains. The renewal theory, queueing theory and realibity theory based on the based on stochastic processes.

Learning Outcomes

#	Content	PC Sub	* Contribution Level
#	Content	PC Sub	1	2	3	4	5
1	Shall be able to understand the nature of stochastic systems
2	Shall be able to create models for stochastic systems
3	Shall be able to analyze a stochastic system

Course Description

Topics of this course include the probability theory, conditional probability and expectation, Exponential distribution, Poisson process, Markov Chains, renewal theory, queueing theory, and realibility theory.

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 to Probability Theory	Textbook Chapter 1
2	Random Variables	Textbook Chapter 2
3	Conditional Probability and Conditional Expectation	Textbook Chapter 3
4	Markov Chains	Textbook Chapter 4
5	Exponential Distribution and the Poisson Process	Textbook Chapter 5
6	Exponential Distribution and the Poisson Process	Textbook Chapter 5
7	Midterm
8	Continuous-Time Markov Chains	Textbook Chapter 6
9	Continuous-Time Markov Chains	Textbook Chapter 6
10	Renewal Theory	Textbook Chapter 7
11	Renewal Theory	Textbook Chapter 7
12	Queueing Theory	Textbook Chapter 8
13	Queueing Theory	Textbook Chapter 8
14	Reliability Theory	Textbook Chapter 9
15	Reliability Theory	Textbook Chapter 9
16	Review of the Semester

Course Notes/Textbooks	Sheldon M. Ross, Introduction to Probability Models, Academic Press. Instructor notes and lecture slides.
Suggested Readings/Materials

EVALUATION SYSTEM

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

Weighting of Semester Activities on the Final Grade		60
Weighting of End-of-Semester Activities on the Final Grade		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	6	90
Field Work			0
Quizzes / Studio Critiques		10	0
Portfolio			0
Homework / Assignments			0
Presentation / Jury			0
Project			0
Seminar / Workshop			0
Oral Exam			0
Midterms	1	20	20
Final Exam	1	27	27
		Total	185

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	-	-	-