ELECTRIC-ELECTRONIC ENGINEERING (ENGLISH, NON-THESIS)
Master TR-NQF-HE: Level 7 QF-EHEA: Second Cycle EQF-LLL: Level 7

Course Introduction and Application Information

Course Code Course Name Semester Theoretical Practical Credit ECTS
CMP5101 Data Mining Fall 3 0 3 8
The course opens with the approval of the Department at the beginning of each semester

Basic information

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

Learning Outputs

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.

Course Content

1. Frequent Item Set Detection
2. Association Rule Mining
3. Clustering
4. Classification

Weekly Detailed Course Contents

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

Sources

Course Notes: Data Mining Concepts and Techniques Jiawei Han and Micheline Kamber Morgan Kaufman
References:

Evaluation System

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

ECTS / Workload Table

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

Contribution of Learning Outcomes to Programme Outcomes

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.
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.
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).
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.
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.