YBS602


Course Title Course Code Program Level
DATA MINING YBS602 Management Information Systems (Doctorate) M.A / M.Sc.

Course Term
(Course Semester)
Teaching and Learning Methods
Credits
Theory Practice Lab Projects/Field Work Seminars/Workshops Other Total Credits ECTS Credits
02
(Spring)
45 55 100 3 8

Teaching Staff
Language of Instruction Türkçe (Turkish)
Type Of Course Compulsory
Prerequisites
Recommended Optional Programme Component
Course Objectives The aim of this course is to introduce the methods used in data mining.
Course Content The basic algorithms used in data mining will be covered and their role in data analysis and analysis will be emphasized.
Learning Outcomes (LO) To gain knowledge and skills about the techniques used in data mining.
Mode of Delivery Face to face
Course Outline
Week Topics
1. Week Introduction to data mining
2. Week Regulation used in various algorithms
3. Week Naive Bayes
4. Week Feature reduction methods in datasets
5. Week Unsupervised machine learning
6. Week Unsupervised machine learning
7. Week Neural Networks concept
8. Week Overview of neural networks algorithms
9. Week Hyper parameter edits
10. Week Tensor Flow approach
11. Week Tensor Flow apps
12. Week Function and graphics
13. Week Natural language and texts
14. Week
Assessment
  Percentage(%)
Mid-term (%) 40
Quizes (%)
Homeworks/Term papers (%) 20
Practice (%)
Labs (%)
Projects/Field Work (%)
Seminars/Workshops (%)
Final (%) 40
Other (%)
Total(%) 100
Course Book (s) and/or References Hands-on machine Learning by Aurelien Geron.
Work Placement(s)
The Relationship between Program Qualifications (PQ) and Course Learning Outcomes (LO)