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GRADUATE INSTITUTE of NATURAL and APPLIED SCIENCES / DEPARTMENT of COMPUTER ENGINEERING
Computer Engineering, Masters with Thesis
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http://ceng.ktu.edu.tr
Phone: +90 0462 3773157
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GRADUATE INSTITUTE of NATURAL and APPLIED SCIENCES / DEPARTMENT of COMPUTER ENGINEERING / Computer Engineering, Masters with Thesis
Katalog Ana Sayfa
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BILL5210Knowledge Discovery in Large Data Sets3+0+0ECTS:7.5
Year / SemesterFall Semester
Level of CourseSecond Cycle
Status Elective
DepartmentDEPARTMENT of COMPUTER ENGINEERING
Prerequisites and co-requisitesNone
Mode of DeliveryFace to face
Contact Hours14 weeks - 3 hours of lectures per week
Lecturer--
Co-Lecturer
Language of instructionTurkish
Professional practise ( internship ) None
 
The aim of the course:
The course intends to teach the students for the principles of knowledge discovery in large data sets, and the ability to use the popular methods in this area.
 
Programme OutcomesCTPOTOA
Upon successful completion of the course, the students will be able to :
PO - 1 : Understand the basic concepts of knowledge discovery and data mining.1,141
PO - 2 : Gain knowledge on how preprocessing methods work.1,11,141
PO - 3 : Design and implement supervised / unsupervised learning methods, outlier detection methods and association rules..1,3,12,141,3
PO - 4 : Design and implement advanced data mining methods.1,3,11,14,151,3
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

 
Contents of the Course
Basic Concepts; Preprocessing Methods; Feature Extraction; Outlier Analysis; Supervised Learning; Statistical Learning Theory; Instance-based Learning; Decision Trees; Clustering; Association Rules; Advances in Data Mining, Advanced Data Mining Methods.
 
Textbook / Material
1O. ve Rokach, L., Data Mining and Knowledge Discovery Handbook, Maimon, Springer, 2010, 1285 sayfa.
 
Recommended Reading
 
Method of Assessment
Type of assessmentWeek NoDate

Duration (hours)Weight (%)
Mid-term exam 9 2 30
Project 15 2 20
End-of-term exam 16 3 50
 
Student Work Load and its Distribution
Type of workDuration (hours pw)

No of weeks / Number of activity

Hours in total per term
Yüz yüze eğitim 3 14 42
Sınıf dışı çalışma 4 14 56
Arasınav için hazırlık 12 1 12
Arasınav 2 1 2
Proje 5 14 70
Dönem sonu sınavı için hazırlık 15 1 15
Dönem sonu sınavı 3 1 3
Total work load200