GRADUATE SCHOOL
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
|
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| Course Language |
English
|
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| Course Type |
Elective
|
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| Course Level |
Second Cycle
|
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| Mode of Delivery | - | |||||
| Teaching Methods and Techniques of the Course | - | |||||
| 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 |
The students who succeeded in this course;
|
| 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. |
|
|
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 |
| 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 | Weigthing |
| 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
|
#
|
Program Competencies/Outcomes |
* Contribution Level
|
||||
|
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 | ||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest