| MATHEMATICS (TURKISH, PHD) | |||||
| PhD | TR-NQF-HE: Level 8 | QF-EHEA: Third Cycle | EQF-LLL: Level 8 | ||
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| CMP6023 | Fundamental Technologies in Data Mining | Fall | 3 | 0 | 3 | 12 |
| The course opens with the approval of the Department at the beginning of each semester |
| Language of instruction: | En |
| Type of course: | Departmental Elective |
| Course Level: | |
| Mode of Delivery: | Face to face |
| Course Coordinator : | Prof. Dr. ADEM KARAHOCA |
| Course Objectives: | The objectives of this course are introducing data mining, data warehouse and OLAP technology for data mining, data processing, mining association rules in large databases, association mining to correlation analysis, cluster analysis, mining complex type of data. |
|
The students who have succeeded in this course; 1. Describe fundamentals of data mining. 2. Analyze probability distributions 3. Analyze linear models for regression and classification. 4. Analyze neural networks. 5. Analyze kernel machines. 6. Analyze and identify data sampling methods. 7. Analyze models for data mining. |
| The content of this course consists of introduction to Data Mining, Probability Distributions, Linear Models for Regression, Linear Models for Classification, Neural Networks, Kernel Methods, Sparse Kernel Machines, Sampling Methods and Graphical Models, Mixture Models and EM, Approximate Inference, Continuous Latent Variables, Sequential Data, Combining Models |
| Week | Subject | Related Preparation | |
| 1) | Introduction to Data Mining | ||
| 2) | Probability Distributions | ||
| 3) | Linear Models for Regression | ||
| 4) | Linear Models for Classification | ||
| 5) | Neural Networks | ||
| 6) | Kernel Methods | ||
| 7) | Sparse Kernel Machines | ||
| 8) | Sparse Kernel Machines / Midterm | ||
| 9) | Sampling Methods and Graphical Models | ||
| 10) | Mixture Models and EM | ||
| 11) | Approximate Inference | ||
| 12) | Continuous Latent Variables | ||
| 13) | Sequential Data | ||
| 14) | Combining Models | ||
| Course Notes: | New Fundamental Technologies in Data Mining, Edited by: Kimito Funatsu, 2011 Pattern Recognition and Machine Learning, Bishop, Christopher M., 2006 |
| References: | Yok |
| Semester Requirements | Number of Activities | Level of Contribution |
| Attendance | 14 | % 10 |
| Laboratory | % 0 | |
| Application | % 0 | |
| Field Work | % 0 | |
| Special Course Internship (Work Placement) | % 0 | |
| Quizzes | 4 | % 10 |
| Homework Assignments | 1 | % 10 |
| Presentation | % 0 | |
| Project | % 0 | |
| Seminar | % 0 | |
| Midterms | 1 | % 30 |
| Preliminary Jury | % 0 | |
| Final | 1 | % 40 |
| Paper Submission | % 0 | |
| Jury | % 0 | |
| Bütünleme | % 0 | |
| Total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 60 | |
| PERCENTAGE OF FINAL WORK | % 40 | |
| Total | % 100 | |
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution |