Master TR-NQF-HE: Level 7 QF-EHEA: Second Cycle EQF-LLL: Level 7

Course Introduction and Application Information

Course Code Course Name Semester Theoretical Practical Credit ECTS
INE5110 Probabilistic Models and Applications Fall 3 0 3 8

Basic information

Language of instruction: English
Type of course: Must Course
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.

Learning Outcomes

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.

Course Content

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.

Weekly Detailed Course Contents

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

Evaluation System

Semester Requirements Number of Activities Level of Contribution
Quizzes 2 % 20
Homework Assignments 5 % 15
Midterms 1 % 25
Final 1 % 40
Total % 100
Total % 100

ECTS / Workload Table

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

Contribution of Learning Outcomes to Programme Outcomes

No Effect 1 Lowest 2 Low 3 Average 4 High 5 Highest
Program Outcomes Level of Contribution
1) Process view and analytic thinking
2) managerial thinking with technical background 3
3) To have theoretical knowledge on operations research. 5
4) Awareness about the applications of operations research 4
5) To have ability of selection and efficient use of modern techniques, equipments and information technologies for industrial engineering 3
6) To be capable of designing and conducting experiments and collecting data, analyzing and interpreting results 1
7) To have verbal and oral effective communication skills by using visual methods in Turkish and English
8) To be aware of entrepreneurship, sustainability and innovation
9) To lead disciplinary and multi-disciplinary teams, to develop solution approaches in complex situations, to work individually and to take responsibility.
10) To have conscious of professional and ethical responsibility
11) To have conscious of necessity to lifelong learning
12) To be aware of economic and legal implications of engineering solutions
13) Economic, social and environmental responsibility while solving management problems