FACULTY OF ENGINEERING
Department of Industrial Engineering
SOCIAL MEDIA
IE 343 | Course Introduction and Application Information
| Course Name |
Data Mining
|
|
Code
|
Semester
|
Theory
(hour/week) |
Application/Lab
(hour/week) |
Local Credits
|
ECTS
|
|
IE 343
|
Fall/Spring
|
3
|
0
|
3
|
5
|
| Prerequisites |
|
|||||||||||
| Course Language |
English
|
|||||||||||
| Course Type |
Elective
|
|||||||||||
| Course Level |
First Cycle
|
|||||||||||
| Mode of Delivery | - | |||||||||||
| Teaching Methods and Techniques of the Course | Lecture / Presentation | |||||||||||
| Course Coordinator | ||||||||||||
| Course Lecturer(s) | ||||||||||||
| Assistant(s) | - | |||||||||||
| Course Objectives | The main objective of this course is to provide a basic understanding of data mining concepts and to use it in data mining software packages, especially in Weka. The course will cover basic approaches in machine learning and data mining. |
| Learning Outcomes |
The students who succeeded in this course;
|
| Course Description | The topics include basic machine learning and data mining methods and principles. |
|
|
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 to Data Mining, Weka Software | Lecture Slides |
| 2 | Weka Installation, Loading and Displaying data, Classification, Creating a Classifier | Lecture Slides |
| 3 | Using Filters, Visualizing Data | Lecture Slides |
| 4 | Evaluating Classifiers, Baseline Accuracy | Lecture Slides |
| 5 | 1. Midterm | |
| 6 | Cross Validation | Lecture Slides |
| 7 | Simple Classifiers, Overfitting | Lecture Slides |
| 8 | Using Probabilities, Decision Trees | Lecture Slides |
| 9 | Nearest Neighbor Algorithm, Using Weka in practice | Lecture Slides |
| 10 | 2. Midterm | |
| 11 | Classification Boundaries, Linear Regression | Lecture Slides |
| 12 | Classification with Regression, Logistic Regression | Lecture Slides |
| 13 | Support Vector Machines, Ensemble Learning | Lecture Slides |
| 14 | Data Mining Process, Pitfalls and Pratfalls, Data Mining and Ethics | Lecture Slides |
| 15 | Review of the Semester | |
| 16 | Final |
| Course Notes/Textbooks | Witten, Ian H., Eibe Frank, and A. Mark. "Hall, and Christopher J Pal. 2016. Data Mining: Practical machine learning tools and techniques.", ISBN: 978-0128042915 |
| Suggested Readings/Materials | Lecture Slides |
EVALUATION SYSTEM
| Semester Activities | Number | Weigthing |
| Participation | ||
| Laboratory / Application | ||
| Field Work | ||
| Quizzes / Studio Critiques | ||
| Portfolio | ||
| Homework / Assignments | ||
| Presentation / Jury | ||
| Project | ||
| Seminar / Workshop | ||
| Oral Exams | ||
| Midterm |
2
|
60
|
| 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 |
|---|---|---|---|
| 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 |
0
|
||
| Presentation / Jury |
0
|
||
| Project |
0
|
||
| Seminar / Workshop |
0
|
||
| Oral Exam |
0
|
||
| Midterms |
2
|
15
|
30
|
| Final Exam |
1
|
30
|
30
|
| 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. |
|||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest
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