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 | Lecturing / 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 | ||
Supportive Courses | ||
Media and Managment Skills Courses | ||
Transferable Skill Courses |
Week | Subjects | Required Materials |
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 |
Semester Activities | Number | Weigthing |
Participation | ||
Laboratory / Application | ||
Field Work | ||
Quizzes / Studio Critiques | ||
Portfolio | ||
Homework / Assignments | ||
Presentation / Jury | ||
Project | ||
Seminar / Workshop | ||
Oral Exam | ||
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 |
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 | 2 | 15 | |
Final Exams | 1 | 30 | |
Total | 150 |
# | Program Competencies/Outcomes | * Contribution Level | ||||
1 | 2 | 3 | 4 | 5 | ||
1 | To have adequate knowledge in Mathematics, Science and Computer Engineering; to be able to use theoretical and applied information in these areas on complex engineering problems. | |||||
2 | To be able to identify, define, formulate, and solve complex Computer Engineering problems; to be able to select and apply proper analysis and modeling methods for this purpose. | |||||
3 | To be able to design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the requirements; to be able to apply modern design methods for this purpose. | |||||
4 | To be able to devise, select, and use modern techniques and tools needed for analysis and solution of complex problems in Computer Engineering applications; to be able to use information technologies effectively. | |||||
5 | To be able to design and conduct experiments, gather data, analyze and interpret results for investigating complex engineering problems or Computer Engineering research topics. | |||||
6 | To be able to work efficiently in Computer Engineering disciplinary and multi-disciplinary 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 global and social impact of Computer Engineering practices on health, environment, and safety; to have knowledge about contemporary issues as they pertain to engineering; to be aware of the legal ramifications of Computer Engineering solutions. | |||||
9 | To be aware of ethical behavior, professional and ethical responsibility; to have knowledge about standards utilized in engineering applications. | |||||
10 | To have knowledge about industrial practices such as project management, risk management, and change management; to have awareness of entrepreneurship and innovation; to have knowledge about sustainable development. | |||||
11 | To be able to collect data in the area of Computer Engineering, and to be able to communicate with colleagues in a foreign language. ("European Language Portfolio Global Scale", Level B1) | |||||
12 | To be able to speak a second foreign language 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 Computer Engineering. |
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest