Institute of Science and Technology · Computer Science and Engineering (%30 English) · Doctorate
ECTS: 7.5 T+P+L: 3+0+0 Departmental Elective
Coordinator: Dr. Öğr. Üyesi Meryem UZUN PER
Course Objective
Feature engineering methods, classification, and clustering methods, which are one of the data mining processes, are aimed to be processed with theoretical and practical applications.
Course Content
Feature engineering methods, classification and clustering processes, and mathematical models will be taught theoretically and practically.Course Learning Outcomes
- Students will be able to apply data mining methods in obtaining valuable data from large-scale data.
- Students will be able to apply classification and clustering methods to produce meaningful results from valuable data.
- Students will be able to apply data mining steps in solving real life problems.
- Students will be able to produce the optimum solution of a complex problem.
- Students will be able to develop mathematical modeling according to the type of data available.
Core Area Distribution
(46) Mathematics and Statistics%40 (52) Engineering and Engineering Trades%60
Teaching Methods
ExpressionQuestion-AnswerPresentationSelf study
Assessment & Evaluation
Performance Assignment ( Lab / Workshop / Field Work / Seminar / Presentation / Completion Study / ThesisProject / DesignTesting (Essay / Tests: True-Falls, multiple-choice, short answer, matching)
ECTS / Workload
| Activity | Quantity | Duration (h) | Total Workload |
|---|---|---|---|
| Course Duration (Including Exam Week) | 14 | 3 | 42 |
| Out of Class Study Period | 14 | 3 | 42 |
| Midterm | 1 | 2 | 2 |
| Quiz | 0 | 0 | 0 |
| Assignment | 1 | 6 | 6 |
| Practice | 1 | 1 | 1 |
| Final | 1 | 3 | 3 |
Course Schedule
| Week | Subject | Preparation |
|---|---|---|
| 1 | Introduction | book |
| 2 | Data Preprocessing | book |
| 3 | Classification methods | book |
| 4 | Classification methods | book |
| 5 | Clustering methods | book |
| 6 | Clustering methods | book |
| 7 | Project First Presentation | book |
| 8 | Midterm exam | lecture notes |
| 9 | Association rules | book |
| 10 | Association rules | book |
| 11 | Sequential patterns | book |
| 12 | Feature Selection Methods | book |
| 13 | Dimension Reduction Methods | book |
| 14 | Project Final Presentation | book |
| 15 | Final Exam | final |
| 16 | Final Exam | final |