ELECTRIC-ELECTRONIC ENGINEERING (ENGLISH, THESIS) | |||||
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 |
This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester. |
Language of instruction: | English |
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 |
Recommended Optional Program Components: | None |
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 / Textbooks: | Data Mining Concepts and Techniques Jiawei Han and Micheline Kamber Morgan Kaufman |
References: |
Semester Requirements | Number of Activities | Level of Contribution |
Project | 5 | % 10 |
Midterms | 1 | % 40 |
Final | 1 | % 50 |
Total | % 100 | |
PERCENTAGE OF SEMESTER WORK | % 40 | |
PERCENTAGE OF FINAL WORK | % 60 | |
Total | % 100 |
Activities | Number of Activities | Workload |
Course Hours | 14 | 42 |
Study Hours Out of Class | 14 | 56 |
Project | 16 | 48 |
Midterms | 3 | 15 |
Final | 7 | 35 |
Total Workload | 196 |
No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
Program Outcomes | Level of Contribution | |
1) | Have sufficient background and an ability to apply knowledge of mathematics, science, and engineering to identify, formulate, and solve problems of electrical and electronics engineering. | 4 |
2) | Be able to define, formulate and solve sophisticated engineering problems by choosing and applying appropriate analysis and modeling techniques and using technical symbols and drawings of electrical and electronics engineering for design, application and communication effectively. | 4 |
3) | Have an ability to design or implement an existing design of a system, component, or process to meet desired needs within realistic constraints (realistic constraints may include economic, environmental, social, political, health and safety, manufacturability, and sustainability issues depending on the nature of the specific design). | 3 |
4) | Elektrik ve elektronik mühendisliği yapabilmek ve yeni uygulamalara uyum gösterebilmek için gerekli yenilikçi ve güncel teknikler, beceriler, bilgi teknolojileri ve modern mühendislik araçlarını geliştirmek, seçmek, uyarlamak ve kullanmak. | |
5) | Be able to design and conduct experiments, as well as to collect, analyze, and interpret relevant data, and use this information to improve designs. | 4 |
6) | Be able to function individually as well as to collaborate with others in multidisciplinary teams. | |
7) | Be able to communicate effectively in English and Turkish (if he/she is a Turkish citizen). | |
8) | Be able to recognize the need for, and to engage in life-long learning as well as a capacity to adapt to changes in the technological environment. | |
9) | Have a consciousness of professional and ethical responsibilities as well as workers’ health, environment and work safety. | |
10) | Have the knowledge of business practices such as project, risk, management and an awareness of entrepreneurship, innovativeness, and sustainable development. | |
11) | Have the broad knowledge necessary to understand the impact of electrical and electronics engineering solutions in a global, economic, environmental, legal, and societal context. |