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 |
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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;
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Course Description | The topics include basic machine learning and data mining methods and principles. |
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 to Data Mining | Lecture Slides |
2 | Data Preprocessing, Types of Data, Data Preparation | Lecture Slides |
3 | Data Warehouses, OLAP, Exploring Data | Lecture Slides |
4 | Classification: Basic Concepts and Techniques | Lecture Slides |
5 | Classification: Overfitting | |
6 | Classification: Rule Based Classifiers, Nearest-Neighbor Classifiers | Lecture Slides |
7 | Classification: Bayesian Classifiers, Artificial NeuralNetworks | Lecture Slides |
8 | Classification: Support Vector Machine, Ensemble Methods | Lecture Slides |
9 | Midterm | |
10 | Association Analysis: Basic Concepts and Algorithms | Lecture Slides |
11 | Association Analysis: Advanced Concepts | Lecture Slides |
12 | Cluster Analysis: Basic Concepts and Algorithms | Lecture Slides |
13 | Cluster Analysis: Additional Issues and Algorithms | Lecture Slides |
14 | Anomaly Detection | Lecture Slides |
15 | Review | |
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 |
Semester Activities | Number | Weighting |
Participation | ||
Laboratory / Application | ||
Field Work | ||
Quizzes / Studio Critiques | ||
Portfolio | ||
Homework / Assignments | 1 | 10 |
Presentation / Jury | ||
Project | 1 | 20 |
Seminar / Workshop | ||
Oral Exam | ||
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 |
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 | |||
Presentation / Jury | |||
Project | |||
Seminar / Workshop | |||
Oral Exam | |||
Midterms | 1 | 15 | |
Final Exams | 1 | 30 | |
Total | 135 |
# | 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