Course Name | Computational Thinking for Operations Research |
Code | Semester | Theory (hour/week) | Application/Lab (hour/week) | Local Credits | ECTS |
---|---|---|---|---|---|
IE 213 | Spring | 3 | 0 | 3 | 5 |
Prerequisites |
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Course Language | English | ||||||||
Course Type | Required | ||||||||
Course Level | First Cycle | ||||||||
Mode of Delivery | Online | ||||||||
Teaching Methods and Techniques of the Course | Lecture / Presentation | ||||||||
Course Coordinator | |||||||||
Course Lecturer(s) | |||||||||
Assistant(s) |
Course Objectives | This course is intended for students with basic programming experience in Python. It aims to provide students with different computational approaches for various Operations Research (OR) problems and to help students feel justifiably confident of their ability to write small programs for solving OR problems. The class will use Python programming language. |
Learning Outcomes | The students who succeeded in this course;
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Course Description | This course focuses on computational thinking for Operations Research. Towards the end of the course, students are also introduced to some basic models used in Machine Learning. |
Related Sustainable Development Goals |
| Core Courses | |
Major Area Courses | X | |
Supportive Courses | ||
Media and Managment Skills Courses | ||
Transferable Skill Courses |
Week | Subjects | Required Materials |
1 | Introduction and Optimization Problems | Introduction to Computation and Programming Using Python Chapters 12.1 and 5.4 |
2 | Optimization Problems | Introduction to Computation and Programming Using Python Chapter 13 |
3 | Graph-theoretic Models | Introduction to Computation and Programming Using Python Chapter 12.2 |
4 | Stochastic Thinking | Introduction to Computation and Programming Using Python Chapter 14 |
5 | Random Walks | Introduction to Computation and Programming Using Python Chapters 11 and 14 |
6 | Monte Carlo Simulation | Introduction to Computation and Programming Using Python Chapters 15.1–15.4 and 16 |
7 | Confidence Intervals | Introduction to Computation and Programming Using Python Chapters 16.4 and 17 |
8 | Midterm Exam | |
9 | Sampling and Standard Error | Introduction to Computation and Programming Using Python Chapter 17 |
10 | Understanding Experimental Data | Introduction to Computation and Programming Using Python Chapter 18 |
11 | Introduction to Machine Learning | Introduction to Computation and Programming Using Python Chapter 22 |
12 | Clustering | Introduction to Computation and Programming Using Python Chapter 23 |
13 | Classification and Statistical Sins | Introduction to Computation and Programming Using Python Chapter 21 and 24 |
14 | Statistical Sins and Wrap Up | Introduction to Computation and Programming Using Python Chapter 21 |
15 | General review | |
16 | Final Exam |
Course Notes/Textbooks | Guttag, John. Introduction to Computation and Programming Using Python: With Application to Understanding Data. 2nd ed. MIT Press, 2016. ISBN: 9780262529624 |
Suggested Readings/Materials | Lecture Slides and Supplementary Codes will be provided. |
Semester Activities | Number | Weigthing |
Participation | ||
Laboratory / Application | ||
Field Work | ||
Quizzes / Studio Critiques | ||
Portfolio | ||
Homework / Assignments | 1 | 30 |
Presentation / Jury | ||
Project | ||
Seminar / Workshop | ||
Oral Exam | ||
Midterm | 1 | 30 |
Final Exam | 1 | 40 |
Total |
Weighting of Semester Activities on the Final Grade | 2 | 60 |
Weighting of End-of-Semester Activities on the Final Grade | 1 | 40 |
Total |
Semester Activities | Number | Duration (Hours) | Workload |
---|---|---|---|
Course Hours (Including exam week: 16 x total hours) | 16 | 3 | 48 |
Laboratory / Application Hours (Including exam week: 16 x total hours) | 16 | ||
Study Hours Out of Class | 14 | 3 | 42 |
Field Work | |||
Quizzes / Studio Critiques | |||
Portfolio | |||
Homework / Assignments | 1 | 15 | |
Presentation / Jury | |||
Project | |||
Seminar / Workshop | |||
Oral Exam | |||
Midterms | 1 | 18 | |
Final Exams | 1 | 27 | |
Total | 150 |
# | Program Competencies/Outcomes | * Contribution Level | ||||
1 | 2 | 3 | 4 | 5 | ||
1 | To have adequate knowledge in Mathematics, Science and Industrial Engineering; to be able to use theoretical and applied information in these areas to model and solve Industrial Engineering problems. | X | ||||
2 | To be able to identify, formulate and solve complex Industrial Engineering problems by using state-of-the-art methods, techniques and equipment; to be able to select and apply proper analysis and modeling methods for this purpose. | X | ||||
3 | To be able to analyze a complex system, process, device or product, and to design with realistic limitations to meet the requirements using modern design techniques. | |||||
4 | To be able to choose and use the required modern techniques and tools for Industrial Engineering applications; to be able to use information technologies efficiently. | X | ||||
5 | To be able to design and do simulation and/or experiment, collect and analyze data and interpret the results for investigating Industrial Engineering problems and Industrial Engineering related research areas. | X | ||||
6 | To be able to work efficiently in Industrial Engineering disciplinary and multidisciplinary teams; to be able to work individually. | |||||
7 | To be able to communicate effectively in Turkish, both orally and in writing; to be able to author and comprehend written reports, to be able to prepare design and implementation reports, to present effectively; to be able to give and receive clear and comprehensible instructions | |||||
8 | To have knowledge about contemporary issues and the global and societal effects of Industrial Engineering practices on health, environment, and safety; to be aware of the legal consequences of Industrial Engineering solutions. | |||||
9 | To be aware of professional and ethical responsibility; to have knowledge of the standards used in Industrial Engineering practice. | |||||
10 | To have knowledge about business life practices such as project management, risk management, and change management; to be aware of entrepreneurship and innovation; to have knowledge about sustainable development. | |||||
11 | To be able to collect data in the area of Industrial Engineering; to be able to communicate with colleagues in a foreign language. | |||||
12 | To be able to speak a second foreign at a medium level of fluency efficiently. | |||||
13 | To recognize the need for lifelong learning; to be able to access information, to be able to stay current with developments in science and technology; to be able to relate the knowledge accumulated throughout the human history to Industrial Engineering. | X |
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest