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| TBB6002 | Advanced Data Mining | 2+2+0 | ECTS:7.5 | | Year / Semester | Fall Semester | | Level of Course | Third Cycle | | Status | Elective | | Department | DEPARTMENT of BIOSTATISTICS and MEDICAL INFORMATICS | | Prerequisites and co-requisites | None | | Mode of Delivery | Face to face, Practical | | Contact Hours | 14 weeks - 2 hours of lectures and 2 hours of practicals per week | | Lecturer | -- | | Co-Lecturer | | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | To teach advanced informations about data mining |
| Programme Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 1 : | Knows data minings process.
| | 5 | | PO - 2 : | Explain Association Rules | | | | PO - 3 : | Knows clustering and classification methods. | | 5 | | PO - 4 : | Use data mining methods in scientific research | | 6 | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), PO : Learning Outcome | | |
| Data Mining Functionalities, Association Rules ,The Apriori Algorithm, Classification and Prediction Algorithm, Decision Trees, Bayes Theorem, Support Vector Machines,Cluster Analysis |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Introduction To Data Mining | | | Week 2 | The Apriori Algorithm and Association Rules | | | Week 3 | Classification and Prediction | | | Week 4 | Classification and Prediction | | | Week 5 | Bayesian Classification | | | Week 6 | Decision Tree | | | Week 7 | Decision Tree | | | Week 8 | Mid term | | | Week 9 | Support Vector Machines | | | Week 10 | Support Vector Machines | | | Week 11 | Genetic Algorithms | | | Week 12 | Model Selection and ROC Curves | | | Week 13 | Clustering Methods | | | Week 14 | Clustering Methods | | | Week 15 | General test preparation | | | Week 16 | Final Exam
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| 1 | Jiawei Han and Micheline Kambe,Data Mining: Concepts and Techniques, Elsevier, 2006 | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | | 2 | 30 | | Practice | 10 | | 1 | 20 | | Homework/Assignment/Term-paper | 15 | | 2 | 10 | | End-of-term exam | 16 | | 2 | 40 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Yüz yüze eğitim | 3 | 14 | 42 | | Sınıf dışı çalışma | 3 | 14 | 42 | | Arasınav | 2 | 5 | 10 | | Uygulama | 2 | 14 | 28 | | Ödev | 4 | 6 | 24 | | Proje | 7 | 1 | 7 | | Dönem sonu sınavı için hazırlık | 4 | 8 | 32 | | Dönem sonu sınavı | 2 | 1 | 2 | | Total work load | | | 187 |
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