| Semester | Course Unit Code | Course Unit Title | T+P+L | Credit | Number of ECTS Credits |
| 1 | ECE565 | DATA MINING | 3+0+0 | 3 | 7,5 |
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Language of Instruction
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English
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Level of Course Unit
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Master's Degree
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Department / Program
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ELECTRICAL AND COMPUTER ENGINEERING
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Type of Program
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Formal Education
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Type of Course Unit
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Elective
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Course Delivery Method
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Face To Face
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Objectives of the Course
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1) To define the steps of knowledge discovery from data.
2) To explain fundamental pattern discovery techniques.
3) To perform classification and clustering algorithms on data mining software.
4) To evaluate the performance of a classification model.
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Course Content
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Introduction to Data Mining
Data Representation and Data Preprocessing
Pattern Discovery
Basic Concepts and Methods of Classification
Techniques to Improve Classification Accuracy
Basic Concepts and Methods of Clustering
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Course Methods and Techniques
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Assignments/Quizzes
Midterm Exam
Term Project
Final Exam
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Prerequisites and co-requisities
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None
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Course Coordinator
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None
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Name of Lecturers
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Asist Prof.Dr. BEKİR HAKAN AKSEBZECİ hakan.aksebzeci@agu.edu.tr
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Assistants
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None
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Work Placement(s)
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No
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Recommended or Required Reading
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Resources
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Introduction to Data Mining (LO1, LO2)
Data Representation and Data Preprocessing (LO1, LO2)
Pattern Discovery (LO3)
Basic Concepts and Methods of Classification (LO4, LO5)
Techniques to Improve Classification Accuracy (LO4)
Basic Concepts and Methods of Clustering (LO4, LO5)
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Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"
Assessment Methods and Criteria
ECTS Allocated Based on Student Workload
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Activities
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Total Work Load
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Yazılı Sınav
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1
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30
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30
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F2F Dersi
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1
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2
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2
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Sunum için Hazırlık
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1
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5
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5
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Proje
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1
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30
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30
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Kısa Sınav
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1
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10
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10
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Okuma
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1
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2
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2
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Araştırma
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1
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10
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10
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Takım/Grup Çalışması
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1
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2
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2
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Final Sınavı
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1
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30
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30
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Total Work Load
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Number of ECTS Credits 4
121
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Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
| No | Learning Outcomes |
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1
| Describe the techniques that are used to represent data and knowledge. |
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2
| Compare the data transformation techniques. |
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3
| Explain frequent itemset detection and association rule mining methods. |
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4
| Interpret the fundamental classification and clustering methods. |
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5
| Perform data mining methods to a real problem using a data mining software. |
Weekly Detailed Course Contents
| Week | Topics | Study Materials | Materials |
| 1 |
Introduction to Data Mining
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| 2 |
Data Representation
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| 3 |
Data Representation
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| 4 |
Data Preprocessing
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| 5 |
Data Preprocessing
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| 6 |
Pattern Discovery
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| 7 |
Pattern Discovery
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| 8 |
Basic Concepts and Methods of Classification
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| 9 |
Basic Concepts and Methods of Classification
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| 10 |
Midterm Exam
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| 11 |
Techniques to Improve Classification Accuracy
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| 12 |
Basic Concepts and Methods of Clustering
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| 13 |
Applications on some Data Mining tools
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| 14 |
Term Project Presentations
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| 15 |
Final Exam
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| 16 |
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Contribution of Learning Outcomes to Programme Outcomes
Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant
https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=77746&lang=en