COURSE INTRODUCTION AND APPLICATION INFORMATION


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
 SE 113To attend the classes (To enrol for the course and get a grade other than NA or W)
Course Language
English
Course Type
Required
Course Level
First Cycle
Mode of Delivery face to face
Teaching Methods and Techniques of the Course Lecture / Presentation
Course Coordinator
Course Lecturer(s)
Assistant(s)
Course Objectives This course is designed for students with basic programming experience in Python. It aims to equip students with different approaches to solving various Operations Research (OR) problems and help them confidently write programs to solve these problems.
Learning Outcomes The students who succeeded in this course;
  • 1. Design algorithms for engineering problems.
  • 2. Practice basic data manipulation using the computation tool.
  • 3. Use advanced tools for the simulation of the data.
  • 4. Use modern software systems and tools.
  • 5. Use statistical data for decision-making.
  • 6. Implement some basic machine learning (clustering, classification, regression, etc.) models.
Course Description The course focuses on numerical and computational thinking for Operations Research. Toward the end of the course, students are also introduced to some basic algorithms used in Machine Learning.
Related Sustainable Development Goals

 



Course Category

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

 

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week Subjects Required Materials
1 Review of Programming with Python Introduction to Computation and Programming Using Python Chapter 5.4
2 Optimization Problems Introduction to Computation and Programming Using Python Chapter 13
3 Optimization Problems Introduction to Computation and Programming Using Python Chapter 13
4 Graphical Problems and Models Introduction to Computation and Programming Using Python Chapter 12.2
5 Stochastic Thinking and Random Walks Introduction to Computation and Programming Using Python Chapters 11 and 14
6 Stochastic Thinking and Random Walks Introduction to Computation and Programming Using Python Chapters 11 and 14
7 Monte Carlo Simulation Introduction to Computation and Programming Using Python Chapters 16.4 and 17
8 Midterm Exam
9 Monte Carlo Simulation Introduction to Computation and Programming Using Python Chapters 16.4 and 17
10 Understanding the 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 Introduction to Computation and Programming Using Python Chapter 21
14 Examples in Machine Learning and General Review Introduction to Computation and Programming Using Python Chapter 21, 22, 23
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.

 

EVALUATION SYSTEM

Semester Activities Number Weighting
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

ECTS / WORKLOAD TABLE

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
20
Final Exams
1
25
    Total
150

 

COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP

#
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