Stochastic Processes (IE508) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Stochastic Processes IE508 Area Elective 3 0 0 3 5
Pre-requisite Course(s)
N/A
Course Language English
Course Type Technical Elective Courses
Course Level Natural & Applied Sciences Master's Degree
Mode of Delivery Face To Face
Learning and Teaching Strategies Lecture, Question and Answer, Problem Solving.
Course Coordinator
Course Lecturer(s)
  • Prof. Dr. Serkan ERYILMAZ
Course Assistants
Course Objectives In this course, a basic understanding of stochastic processes that can be used for practical applications will be given to the students.
Course Learning Outcomes The students who succeeded in this course;
  • Acquaintance of students with the fundamental concepts of stochastic processes.
  • Ability of students to develop an insight about the role of stochastic processes for different engineering disciplines.
  • Ability of students to model, solve and evaluate real life problems using stochastic processes.
Course Content The definitions and the classifications of stochastic processes, Poisson process, renewal theory, Markov chains and processes, applications in reliability and inventory problems.

Weekly Subjects and Releated Preparation Studies

Week Subjects Preparation
1 A review of probability theory
2 Basic Foundations of Stochastic Processes
3 Bernoulli Processes
4 Stochastic processes with independent increments, Wiener process and Poisson process
5 Non-homogeneous and compound Poisson processes
6 Discrete time Markov chains, random walks, branching processes.
7 Continuous-time Markov processes
8 Kolmogorov's differential equations
9 Birth and death processes, applications to Markov queueing models
10 Renewal processes
11 Midterm
12 Reliability applications
13 Inventory problems
14 Selected topics from stationary processes and time series
15 Final Examination Period
16 Dönem Sonu Sınav Çalışmaları

Sources

Course Book 1. “Stochastic Processes” by Sheldon M.Ross.
Other Sources 2. “An Introduction to Stochastic Modeling” by S. Karlin and H.E. Taylor.

Evaluation System

Requirements Number Percentage of Grade
Attendance/Participation - -
Laboratory - -
Application - -
Field Work - -
Special Course Internship - -
Quizzes/Studio Critics - -
Homework Assignments - -
Presentation - -
Project 1 30
Report - -
Seminar - -
Midterms Exams/Midterms Jury 1 30
Final Exam/Final Jury 1 40
Toplam 3 100
Percentage of Semester Work
Percentage of Final Work 100
Total 100

Course Category

Core Courses
Major Area Courses
Supportive Courses X
Media and Managment Skills Courses
Transferable Skill Courses

The Relation Between Course Learning Competencies and Program Qualifications

# Program Qualifications / Competencies Level of Contribution
1 2 3 4 5
1 Ability to apply the acquired knowledge in mathematics, science and engineering X
2 Ability to identify, formulate and solve complex engineering problems X
3 Ability to accomplish the integration of systems X
4 Ability to design, develop, implement and improve complex systems, components, or processes X
5 Ability to select/develop and use suitable modern engineering techniques and tools X
6 Ability to design/conduct experiments and collect/analyze/interpret data X
7 Ability to function independently and in teams X
8 Ability to make use of oral and written communication skills effectively X
9 Ability to recognize the need for and engage in life-long learning X
10 Ability to understand and exercise professional and ethical responsibility X
11 Ability to understand the impact of engineering solutions X
12 Ability to have knowledge of contemporary issues X

ECTS/Workload Table

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours) 16 3 48
Laboratory
Application
Special Course Internship
Field Work
Study Hours Out of Class 16 1 16
Presentation/Seminar Prepration
Project 1 4 4
Report
Homework Assignments 4 4 16
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury 1 16 16
Prepration of Final Exams/Final Jury 1 25 25
Total Workload 125