COMPUTER ENGINEERING (ENGLISH, NON-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 | 3 | 0 | 3 | 8 |
Language of instruction: | English |
Type of course: | Must Course |
Course Level: | |
Mode of Delivery: | Face to face |
Course Coordinator : | Assist. Prof. TEVFİK AYTEKİN |
Course Lecturer(s): |
Assist. Prof. 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) | Define and manipulate advanced concepts of Computer Engineering | 5 |
2) | Use math, science, and modern engineering tools to formulate and solve advenced engineering problems | 5 |
3) | Notice, detect, formulate and solve new engineering problems. | 5 |
4) | Review the literature critically pertaining to his/her research projects, and connect the earlier literature to his/her own results | 4 |
5) | Follow, interpret and analyze scientific researches in the field of engineering and use the knowledge in his/her field of study | 4 |
6) | Work effectively in multi-disciplinary research teams | 4 |
7) | Acquire scientific knowledge | 4 |
8) | Find out new methods to improve his/her knowledge. | 4 |
9) | Effectively express his/her research ideas and findings both orally and in writing | 2 |
10) | Defend research outcomes at seminars and conferences. | 2 |
11) | Prepare master thesis and articles about thesis subject clearly on the basis of published documents, thesis, etc. | 1 |
12) | Demonstrate professional and ethical responsibility. | 3 |
13) | Develop awareness for new professional applications and ability to interpret them. | 2 |