ECTS - Statistical Applications in Industrial Engineering
Statistical Applications in Industrial Engineering (IE442) Course Detail
| Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| Statistical Applications in Industrial Engineering | IE442 | Area Elective | 3 | 0 | 0 | 3 | 5 |
| Pre-requisite Course(s) |
|---|
| N/A |
| Course Language | English |
|---|---|
| Course Type | Elective Courses |
| Course Level | Natural & Applied Sciences Master's Degree |
| Mode of Delivery | Face To Face |
| Learning and Teaching Strategies | Lecture, Demonstration, Experiment, Problem Solving. |
| Course Lecturer(s) |
|
| Course Objectives | The course aims to prepare the student to analyze and classify data and develop empirical models for industrial engineering problems under service/production contexts. The student will be able to distinguish between different statistical techniques and implement them using a statistical software package. |
| Course Learning Outcomes |
The students who succeeded in this course;
|
| Course Content | Applications of simple and multiple linear regression, design and analysis of experiments, multivariate analysis and nonparametric tests for the solution of industrial engineering problems. |
Weekly Subjects and Releated Preparation Studies
| Week | Subjects | Preparation |
|---|---|---|
| 1 | Syllabus Introduction | |
| 2 | Optimization and Algorithms: Basic Definitions | Talbi, Chapter 1.1, 1.3 |
| 3 | Mathematical Modeling with Gurobi | Winston, Chapters 3, 4 |
| 4 | Mathematical Modeling with Gurobi | Winston, Chapters 3, 4 |
| 5 | Metaheuristics | Talbi, Chapter 1.4 |
| 6 | Metaheuristics: Single-solution-based methods | Talbi, Chapter 2 |
| 7 | Metaheuristics: Single-solution-based methods | Talbi, Chapter 2 |
| 8 | Metaheuristics: Population-based methods | Talbi, Chapter 3 |
| 9 | Metaheuristics: Population-based methods | Talbi, Chapter 3 |
| 10 | Midterm | |
| 11 | Introduction to Machine Learning | James, Chapter 2 |
| 12 | Fundamental Machine Learning Algorithms | James, Chapters 3, 4, 8, 9 |
| 13 | Fundamental Machine Learning Algorithms | James, Chapters 3, 4, 8, 9 |
| 14 | Neural Networks | James, Chapter 10.1 |
| 15 | Neural Networks | James, Chapter 10.1 |
| 16 | Final Exam |
Sources
| Course Book | 1. James, G., Witten, D., Hastie, T., and Tibshirani, R. (2023). An introduction to statistical learning: with applications in Python, New York: springer. |
|---|---|
| 2. W. L. Winston, Operations Research: Applications and Algorithm., 4th Edition, Thomson Learning, Inc. 2004. | |
| 3. Talbi, E. G. (2009). Metaheuristics: from design to implementation. John Wiley & Sons. | |
| Other Sources | 4. Müller, A. C., & Guido, S. (2016). Introduction to machine learning with Python: a guide for data scientists. " O'Reilly Media, Inc.". |
| 5. Géron, A. (2022). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. " O'Reilly Media, Inc.". | |
| 6. Chong, E. K., Lu, W. S., & Zak, S. H. (2023). An introduction to optimization: with applications to machine learning. John Wiley & Sons. | |
| 7. Sarkar, R. (2023). A Handbook of Mathematical Models with Python: Elevate your machine learning projects with NetworkX, PuLP, and linalg. Packt Publishing Ltd. | |
| 8. Burkov, A. (2019). The hundred-page machine learning book (Vol. 1). Quebec City, QC, Canada: Andriy Burkov. |
Evaluation System
| Requirements | Number | Percentage of Grade |
|---|---|---|
| Attendance/Participation | - | - |
| Laboratory | - | - |
| Application | - | - |
| Field Work | - | - |
| Special Course Internship | - | - |
| Quizzes/Studio Critics | 2 | 15 |
| Homework Assignments | - | - |
| Presentation | - | - |
| Project | - | - |
| Report | - | - |
| Seminar | - | - |
| Midterms Exams/Midterms Jury | 1 | 35 |
| Final Exam/Final Jury | 1 | 50 |
| Toplam | 4 | 100 |
| Percentage of Semester Work | 50 |
|---|---|
| Percentage of Final Work | 50 |
| Total | 100 |
Course Category
| Core Courses | X |
|---|---|
| Major Area Courses | |
| Supportive Courses | |
| 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 | Attains knowledge through wide and in-depth investigations his/her field and surveys, evaluates, interprets, and applies the knowledge thus acquired. | X | ||||
| 2 | Has a critical and comprehensive knowledge of contemporary engineering techniques and methods of application. | X | ||||
| 3 | By using unfamiliar, ambiguous, or incompletely defined data, completes and utilizes the required knowledge by scientific methods; is able to fuse and make use of knowledge from different disciplines. | |||||
| 4 | Has the awareness of new and emerging technologies in his/her branch of engineering profession, studies and learns these when needed. | |||||
| 5 | Defines and formulates problems in his/her branch of engineering, develops methods of solution, and applies innovative methods of solution. | X | ||||
| 6 | Devises new and/or original ideas and methods; designs complex systems and processes and proposes innovative/alternative solutions for their design. | |||||
| 7 | Has the ability to design and conduct theoretical, experimental, and model-based investigations; is able to use judgment to solve complex problems that may be faced in this process. | |||||
| 8 | Functions effectively as a member or as a leader in teams that may be interdisciplinary, devises approaches of solving complex situations, can work independently and can assume responsibility. | X | ||||
| 9 | Has the oral and written communication skills in one foreign language at the B2 general level of European Language Portfolio. | X | ||||
| 10 | Can present the progress and the results of his investigations clearly and systematically in national or international contexts both orally and in writing. | |||||
| 11 | Knows social, environmental, health, safety, and legal dimensions of engineering applications as well as project management and business practices; and is aware of the limitations and the responsibilities these impose on engineering practices. | X | ||||
| 12 | Commits to social, scientific, and professional ethics during data acquisition, interpretation, and publication as well as in all professional activities. | |||||
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 | 14 | 4 | 56 |
| Presentation/Seminar Prepration | |||
| Project | |||
| Report | |||
| Homework Assignments | |||
| Quizzes/Studio Critics | 2 | 5 | 10 |
| Prepration of Midterm Exams/Midterm Jury | 1 | 5 | 5 |
| Prepration of Final Exams/Final Jury | 1 | 6 | 6 |
| Total Workload | 125 | ||