| INDUSTRIAL ENGINEERING (ENGLISH, THESIS) | |||||
| 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 | Spring | 3 | 0 | 3 | 8 |
| Language of instruction: | English |
| Type of course: | Must Course |
| Course Level: | |
| Mode of Delivery: | Face to face |
| Course Coordinator : | |
| Course Lecturer(s): |
Assoc. Prof. 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 | |
| 1) | Follows the scientific literature, analyzes it critically, and uses it effectively in solving engineering problems. | |
| 2) | Designs, plans, implements, and manages original projects related to the program field. | |
| 3) | Independently conducts studies related to the program field, assumes scientific responsibility, and evaluates the results with a critical perspective. | |
| 4) | Presents the results of their research and projects effectively in written, oral, and visual formats in accordance with academic standards. | |
| 5) | Conducts independent research on subjects requiring expertise in their field, develops original ideas, and transfers this knowledge into practice. | |
| 6) | Effectively uses advanced theoretical and practical knowledge specific to the program field. | |
| 7) | Acts in accordance with professional, scientific, and ethical values; takes responsibility by considering the social, environmental, and ethical impacts of engineering practices. |