FACULTY OF ENGINEERING

Department of Industrial Engineering

IE 213 | 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
Fall
3
0
3
5

Prerequisites
  SE 113 To 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 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;
  • 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 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.

 



Course Category

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

 

WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES

Week Subjects Related Preparation
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.

 

EVALUATION SYSTEM

Semester Activities Number Weigthing
Participation
1
5
Laboratory / Application
Field Work
Quizzes / Studio Critiques
Portfolio
Homework / Assignments
1
25
Presentation / Jury
Project
Seminar / Workshop
Oral Exams
Midterm
1
30
Final Exam
1
40
Total

Weighting of Semester Activities on the Final Grade
3
60
Weighting of End-of-Semester Activities on the Final Grade
1
40
Total

ECTS / WORKLOAD TABLE

Semester Activities Number Duration (Hours) Workload
Theoretical Course Hours
(Including exam week: 16 x total hours)
16
3
48
Laboratory / Application Hours
(Including exam week: '.16.' x total hours)
16
0
Study Hours Out of Class
14
3
42
Field Work
0
Quizzes / Studio Critiques
0
Portfolio
0
Homework / Assignments
1
15
15
Presentation / Jury
0
Project
0
Seminar / Workshop
0
Oral Exam
0
Midterms
1
18
18
Final Exam
1
27
27
    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

 


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