INDUSTRY 4.0 (ENGLISH, NONTHESIS) | |||||
Master | TR-NQF-HE: Level 7 | QF-EHEA: Second Cycle | EQF-LLL: Level 7 |
Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
CMP5101 | Data Mining | Fall Spring |
3 | 0 | 3 | 8 |
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 TEVFİK AYTEKİN |
Course Lecturer(s): |
Dr. Öğr. Üyesi TEVFİK AYTEKİN |
Course Objectives: | This course provides an introduction to data mining concepts. Basic concepts in data mining: frequent item set detection, association rules, clustering and classification are covered in depth |
The students who have succeeded in this course; I. Be able to approach data mining as a process, by demonstrating competency in the use of CRISP-DM, the Cross-Industry Standard Process or Data Mining, including the business understanding phase, the data understanding phase, the exploratory data analysis phase, the modeling phase, the evaluation phase, and the deployment phase. II. Be proficient with leading data mining software, including WEKA III. Understand and apply a wide range of clustering, estimation, prediction, and classification algorithms, including k-means clustering, BIRCH clustering, Kohonen clustering, classification and regression trees, the C4.5 algorithm, logistic Regression, k-nearest neighbor IV. Understand and apply the most current data mining techniques and applications, such as text mining, mining genomics data, and other current issues. V. Understand the mathematical statistics foundations of the algorithms outlined above VI. Evaluate current research and advanced topics in data mining. |
1. Frequent Item Set Detection 2. Association Rule Mining 3. Clustering 4. Classification |
Week | Subject | Related Preparation | |
1) | Introduction to Data Mining | None | |
2) | Frequent Item Set Mining | None | |
3) | Various frequent item set algorithms: Apriori, FPGrowth | None | |
4) | Association Rule Mining | None | |
5) | Classification | None | |
6) | Bayesian classification | None | |
7) | Midterm Exam 1 | Review all the topics | |
8) | Rule based classification | None | |
9) | Cluster Analysis | None | |
10) | k-means | None | |
11) | k-medoids | None | |
12) | Hierarchical clustering | None | |
13) | Cluster quality | None | |
14) | Combining Multiple Clusterings | None |
Course Notes: | Data Mining Concepts and Techniques Jiawei Han and Micheline Kamber Morgan Kaufman |
References: |
Semester Requirements | Number of Activities | Level of Contribution |
Attendance | % 0 | |
Laboratory | % 0 | |
Application | % 0 | |
Field Work | % 0 | |
Special Course Internship (Work Placement) | % 0 | |
Quizzes | % 0 | |
Homework Assignments | % 0 | |
Presentation | % 0 | |
Project | 5 | % 10 |
Seminar | % 0 | |
Midterms | 1 | % 40 |
Preliminary Jury | % 0 | |
Final | 1 | % 50 |
Paper Submission | % 0 | |
Jury | % 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 | 14 | 56 | |
Presentations / Seminar | |||
Project | 16 | 48 | |
Homework Assignments | |||
Quizzes | |||
Preliminary Jury | |||
Midterms | 3 | 15 | |
Paper Submission | |||
Jury | |||
Final | 7 | 35 | |
Total Workload | 196 |
No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
Program Outcomes | Level of Contribution |