Syllabus ( CSE 557 )
|
|
Basic information
|
|
| Course title: |
Data Mining |
| Course code: |
CSE 557 |
| Lecturer: |
Assist. Prof. Burcu YILMAZ
|
| ECTS credits: |
7.5 |
| GTU credits: |
3 (3+0+0) |
| Year, Semester: |
1/2, Fall and Spring |
| Level of course: |
Second Cycle (Master's) |
| Type of course: |
Area Elective
|
| Language of instruction: |
Turkish
|
| Mode of delivery: |
Face to face , Lab work
|
| Pre- and co-requisites: |
CSE 454 |
| Professional practice: |
No |
| Purpose of the course: |
The purpose of this course is to teach students how to make large data more benefitable and perform the processes required for providing the useful knowledge to decision support systems. It is aimed to teach students how to detect the information, pattern and rules hidden in data and convert the data in understable form and evaluate the findings. |
|
|
|
Learning outcomes
|
|
Upon successful completion of this course, students will be able to:
-
Differentiate between useful and useless information in large data stack
Contribution to Program Outcomes
-
Define and manipulate advanced concepts of Computer Engineering
-
Use advanced knowledge of mathematics, science, and engineering
Method of assessment
-
Written exam
-
Detect information, pattern, and rules hidden in large data stack
Contribution to Program Outcomes
-
Formulate and solve advanced engineering problems
-
Use advanced knowledge of mathematics, science, and engineering
Method of assessment
-
Written exam
-
Homework assignment
-
Laboratory exercise/exam
-
Analyze the performance of data mining methods and interpret the outcomes of these methods.
Contribution to Program Outcomes
-
Use advanced knowledge of mathematics, science, and engineering
-
Follow, interpret and analyze scientific researches in the field of engineering and use the knowledge in his/her field of study
Method of assessment
-
Homework assignment
-
Laboratory exercise/exam
-
Use the methods that extract useful information from large data stack
Contribution to Program Outcomes
-
Define and manipulate advanced concepts of Computer Engineering
-
Find out new methods to improve his/her knowledge.
Method of assessment
-
Written exam
-
Homework assignment
-
List the steps for extraction of useful information in the data
Contribution to Program Outcomes
-
Formulate and solve advanced engineering problems
-
Use advanced knowledge of mathematics, science, and engineering
Method of assessment
-
Written exam
|
|
|
Contents
|
|
| Week 1: |
Introduction |
| Week 2: |
Data |
| Week 3: |
Exploration |
| Week 4: |
Classification |
| Week 5: |
Advanced issues on Classification |
| Week 6: |
Association Rules and Advanced issues on Association Rules |
| Week 7: |
Midterm exam |
| Week 8: |
Clustering |
| Week 9: |
Advanced issues on Clustering |
| Week 10: |
Anomaly detection |
| Week 11: |
Web Mining |
| Week 12: |
Text Mining |
| Week 13: |
Data Mining based Intrusion Detection |
| Week 14: |
The other issues |
| Week 15*: |
Applications to the criminal identification in social networks. |
| Week 16*: |
Final exam |
| Textbooks and materials: |
Data Mining: Concepts and Techniques, By Han, Jiawei, Kamber, Micheline, 1964- Published 2001, Morgan Kaufmann, Data mining, 550 pages, ISBN 1558604898
|
| Recommended readings: |
Principles of Data Mining (Adaptive Computation and Machine Learning) (Hardcover) by David J. Hand (Author), Heikki Mannila (Author), Padhraic Smyth (Author)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems) (Paperback) by Ian H. Witten (Author), Eibe Frank (Author)
Data Mining Solutions Methods and Tools for Solving Real World Problems, Christopher Westphal, Teresa Blaxton, Wiley
Kavram ve Algoritmalarıyla Temel Veri Madenciliği, Dr. Gökhan Silahtaroğlu, Birinci Baskı, Papatya Yayınları |
|
|
* Between 15th and 16th weeks is there a free week for students to prepare for final exam.
|
|
|
|
|
Assessment
|
|
|
| Method of assessment |
Week number |
Weight (%) |
|
| Mid-terms: |
7 |
35 |
| Other in-term studies: |
0 |
0 |
| Project: |
0 |
0 |
| Homework: |
3,6,9,12 |
25 |
| Quiz: |
0 |
0 |
| Final exam: |
16 |
40 |
| |
Total weight: |
(%) |
|
|
|
|
Workload
|
|
|
| Activity |
Duration (Hours per week) |
Total number of weeks |
Total hours in term |
|
| Courses (Face-to-face teaching): |
3 |
14 |
|
| Own studies outside class: |
3 |
14 |
|
| Practice, Recitation: |
0 |
0 |
|
| Homework: |
8 |
8 |
|
| Term project: |
0 |
0 |
|
| Term project presentation: |
0 |
0 |
|
| Quiz: |
0 |
0 |
|
| Own study for mid-term exam: |
12 |
1 |
|
| Mid-term: |
1 |
1 |
|
| Personal studies for final exam: |
20 |
1 |
|
| Final exam: |
2 |
1 |
|
| |
|
Total workload: |
|
| |
|
Total ECTS credits: |
* |
|
|
* ECTS credit is calculated by dividing total workload by 25.
(1 ECTS = 25 work hours)
|
|
|
-->
