| 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 |
| CMP4336 | Introduction to Data Mining | Fall | 3 | 0 | 3 | 6 |
| 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 : | Dr. Öğr. Üyesi CEMAL OKAN ŞAKAR |
| Course Objectives: | In this course, data mining algorithms and computational paradigms that are used to extract useful knowledge, extract patterns and regularities in databases, and perform prediction and forecasting will be discussed. Supervised and unsupervised learning approaches will be covered with a focus on pattern discovery and cluster analysis. |
|
The students who have succeeded in this course; 1. Be able to understand Data Gathering and Pre-processing 2. Become familiar with Frequent Item Set Detection 3. Be able to understand Association Rule Mining 4. Be able to understand Classifiers, and their benefits 5. Be able to use Clustering 6. Be able to understand Clustering Evaluation |
| 1.Introduction to Basic Concepts 2.Data Exploration 3.Classification 4.Clustering 5.Dimensionality Reduction 6.Frequent Item Set Mining 7.Association Rule Mining |
| Week | Subject | Related Preparation | |
| 1) | Introduction to Basic Concepts | None | |
| 2) | Data Exploration: Summary Statistics, Visualization, OLAP and Multi-dimensional Data Analysis | None | |
| 3) | Data Pre-Processing, Transformation, Normalization, Standardization | None | |
| 4) | Classification and Regression: Model Selection and Generalization, Decision Trees, Performance Evaluation | None | |
| 5) | Classification: Bayesian Decision Theory, Parametric Classification, Naive Bayes Classifier, Instance-Based Classifiers | ||
| 6) | Classification | None | |
| 6) | Classification and Regression: Artificial Neural Networks, Support Vector Machines | ||
| 7) | Midterm I | Review of all topics covered so far | |
| 8) | Clustering: Partitioning and Hierarchical Algorithms | None | |
| 9) | Clustering: Density-Based Algorithms | ||
| 10) | Cluster Evaluation, Comparing Clusterings | None | |
| 11) | Midterm II | none | |
| 12) | Dimensionality Reduction | none | |
| 13) | Frequent Item Set Mining | none | |
| 14) | Association Rule Mining | none | |
| Course Notes: | Introduction to Data Mining by Pang-Ning Tan, Michael Steinbach and Vipin Kumar |
| References: | Data Mining: Concepts and Techniques, by Jiawei Han, Micheline Kamber and Jian Pei |
| Semester Requirements | Number of Activities | Level of Contribution |
| Attendance | 0 | % 0 |
| Laboratory | 0 | % 0 |
| Application | 0 | % 0 |
| Field Work | 0 | % 0 |
| Special Course Internship (Work Placement) | 0 | % 0 |
| Quizzes | 0 | % 0 |
| Homework Assignments | 2 | % 20 |
| Presentation | 0 | % 0 |
| Project | 1 | % 20 |
| Seminar | 0 | % 0 |
| Midterms | 2 | % 20 |
| Preliminary Jury | 0 | % 0 |
| Final | 1 | % 40 |
| Paper Submission | 0 | % 0 |
| Jury | 0 | % 0 |
| Bütünleme | % 0 | |
| Total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 40 | |
| PERCENTAGE OF FINAL WORK | % 60 | |
| Total | % 100 | |
| Activities | Number of Activities | Workload | |
| Course Hours | 14 | 42 | |
| Laboratory | |||
| Application | |||
| Special Course Internship (Work Placement) | |||
| Field Work | |||
| Study Hours Out of Class | 16 | 32 | |
| Presentations / Seminar | |||
| Project | 5 | 15 | |
| Homework Assignments | 6 | 12 | |
| Quizzes | |||
| Preliminary Jury | |||
| Midterms | 8 | 28 | |
| Paper Submission | |||
| Jury | |||
| Final | 6 | 26 | |
| Total Workload | 155 | ||
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution |