ACTUARIAL SCIENCE (TURKISH, NON-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 | Fall | 3 | 0 | 3 | 8 |
The course opens with the approval of the Department at the beginning of each semester |
Language of instruction: | En |
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 |
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: | 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 |
Attendance | % 0 | |
Laboratory | % 0 | |
Application | % 0 | |
Field Work | % 0 | |
Special Course Internship (Work Placement) | % 0 | |
Quizzes | 2 | % 20 |
Homework Assignments | 5 | % 15 |
Presentation | % 0 | |
Project | % 0 | |
Seminar | % 0 | |
Midterms | 1 | % 25 |
Preliminary Jury | % 0 | |
Final | 1 | % 40 |
Paper Submission | % 0 | |
Jury | % 0 | |
Bütünleme | % 0 | |
Total | % 100 | |
PERCENTAGE OF SEMESTER WORK | % 60 | |
PERCENTAGE OF FINAL WORK | % 40 | |
Total | % 100 |
Activities | Number of Activities | Workload | |
Course Hours | 13 | 37 | |
Laboratory | |||
Application | |||
Special Course Internship (Work Placement) | |||
Field Work | |||
Study Hours Out of Class | 15 | 104 | |
Presentations / Seminar | |||
Project | |||
Homework Assignments | 5 | 40 | |
Quizzes | 2 | 2 | |
Preliminary Jury | |||
Midterms | 1 | 3 | |
Paper Submission | |||
Jury | |||
Final | 1 | 3 | |
Total Workload | 189 |
No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
Program Outcomes | Level of Contribution | |
1) | Acquire the quantitative skills to become an actuary. | |
2) | Will know about risks and ways to manage risk. | |
3) | Will know about financial planning and its role in actuarial management. | |
4) | Will be able to design new products and carry profitability tests and scenario analyses. | |
5) | Besides gaining competence in theoretical subjects, the graduate will also be aware of practical issues and applications through lecturers and instructors who have market experience. | |
6) | Will be able to follow all innovations and carry on research on the particular area. | |
7) | Will share information with colleagues and will use it for project development.. | |
8) | Will be able to apply and make the necessary adaptation to all new rules and regulations. |