| INFORMATION TECHNOLOGIES (ENGLISH, NONTHESIS) | |||||
| Master | TR-NQF-HE: Level 7 | QF-EHEA: Second Cycle | EQF-LLL: Level 7 | ||
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| INE5110 | Probabilistic Models and Applications | Fall | 3 | 0 | 3 | 8 |
| This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester. |
| Language of instruction: | English |
| Type of course: | Departmental Elective |
| Course Level: | |
| Mode of Delivery: | Face to face |
| Course Coordinator : | Dr. Öğr. Üyesi ETHEM ÇANAKOĞLU |
| Course Lecturer(s): |
Dr. Öğr. Üyesi ETHEM ÇANAKOĞLU |
| Recommended Optional Program Components: | N.A. |
| Course Objectives: | The objective of this course is to develop and extend the knowledge of mathematical techniques underlying the application of probability theory to well known engineering and operations research problems. This Ms level course is intended to cover stochastic models such as probability theory, random numbers, conditional probability, Markov processes, and Poisson processes. |
|
The students who have succeeded in this course; I. Identify the nature of probability models. II. Develop a model for a probabilistic real-life problem. III. Solve a constructed probabilistic model analytically. IV. Analyze stochastic processes in real-life. |
| This course is offered in the Industrial Engineering master curriculum as a must course where the basic and fundamental knowledge of stochastic processes are given. After gaining knowledge of these topics, the students will be able to explore research and to do master theses related with both the theory and applications of the probabilistic models covered in this course. |
| Week | Subject | Related Preparation |
| 1) | Introduction | |
| 2) | Random Variables | |
| 3) | Jointly Distributed Random Variables | |
| 4) | Conditional Probability | |
| 5) | Conditional Conditional Expectation | |
| 6) | Markov Chains | |
| 7) | Limiting Probabilities of Markov Chains | |
| 8) | Applications of Markov Chains | |
| 9) | Midterm Exam | |
| 10) | Exponential Distribution | |
| 11) | Poisson Process | |
| 12) | Markov Process | |
| 13) | Limiting Probabilities of Markov Process | |
| 14) | Queuing Theory | |
| 15) | Final exam preparation | |
| 16) | Final Exam |
| Course Notes / Textbooks: | Introduction to Probability Models, 10th ed. Sheldon M. Ross. Academic Press, 2010 978-0-12-375686-2 |
| References: | Stochastic Processes, 2nd ed. Sheldon M. Ross. Wiley, 1996 0-471-12062-6 |
| Semester Requirements | Number of Activities | Level of Contribution |
| Quizzes | 2 | % 20 |
| Homework Assignments | 5 | % 15 |
| Midterms | 1 | % 25 |
| Final | 1 | % 40 |
| Total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 60 | |
| PERCENTAGE OF FINAL WORK | % 40 | |
| Total | % 100 | |
| Activities | Number of Activities | Workload |
| Course Hours | 13 | 37 |
| Study Hours Out of Class | 15 | 104 |
| Homework Assignments | 5 | 40 |
| Quizzes | 2 | 2 |
| Midterms | 1 | 3 |
| Final | 1 | 3 |
| Total Workload | 189 | |
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution |