| Teaching Staff
|
Prof.Dr. Aral EGE |
| 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) |
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