ECTS - Advanced Data Mining
Advanced Data Mining (CMPE566) Course Detail
| Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| Advanced Data Mining | CMPE566 | Area Elective | 3 | 0 | 0 | 3 | 5 |
| Pre-requisite Course(s) |
|---|
| N/A |
| Course Language | English |
|---|---|
| Course Type | Computer Engineering Elective Courses |
| Course Level | Ph.D. |
| Mode of Delivery | Face To Face |
| Learning and Teaching Strategies | Lecture. |
| Course Lecturer(s) |
|
| Course Objectives | To develop an understanding of basic data mining concepts ,the strengths and limitations of popular data mining techniques, and to be able to identify promising business applications of data mining. |
| Course Learning Outcomes |
The students who succeeded in this course;
|
| Course Content | Introduction to data mining, concepts, attributes and instance, data processing (cleaning, integration and reduction), data warehousing and online analytical processing (OLAP), data mining algorithms, credibility, advanced pattern mining, classification, engineering the input and output, data mining software and applications. |
Weekly Subjects and Releated Preparation Studies
| Week | Subjects | Preparation |
|---|---|---|
| 1 | Introduction to Data Mining Lecture Notes | Chapter 1 (Text Book 1) |
| 2 | Input: Concepts, attributes and instance | Lecture Notes Chapter 2 (Text Book 2) |
| 3 | Data Processing (Cleaning, Integration and Reduction) | Lecture Notes Chapter 3 (Text Book 1) |
| 4 | Data Warehousing and Online Analytical Processing (OLAP) | Lecture Notes Chapter 4 (Text Book 1) |
| 5 | Data Mining Algorithms: Basic Methods | Lecture Notes Chapter 4 (Text Book 2) |
| 6 | Credibility: Evaluating what’s been learned | Lecture Notes Chapter 5 (Text Book 2) |
| 7 | Credibility: Evaluating what’s been learned | Lecture Notes Chapter 5 (Text Book 2) |
| 8 | Advanced Pattern Mining | Lecture Notes Chapter 7 (Text Book 1) |
| 9 | Advanced Pattern Mining | Lecture Notes Chapter 7 (Text Book 1) |
| 10 | Classification: Basic Concepts | Lecture Notes Chapter 8 (Text Book 1) |
| 11 | Classification: Basic Concepts | Lecture Notes Chapter 8 (Text Book 1) |
| 12 | Transformations: Engineering the Input and Output | Lecture Notes Chapter 7 (Text Book 2) |
| 13 | Transformations: Engineering the Input and Output | Lecture Notes Chapter 7 (Text Book 2) |
| 14 | Advanced techniques, Data Mining software and applications | Lecture Notes Chapter 12 (Text Book 2) |
| 15 | Review | |
| 16 | Review |
Sources
| Course Book | 1. Jiawei Han and Micheline Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, 2006. |
|---|---|
| 2. Ian H. Witten and Eibe Frank. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann Publishers, 2005. | |
| 3. Pang-Ning Tan, Michael Steinbach and Vipin Kumar. Introduction to Data Mining. Addison Wesley, 2005. | |
| Other Sources | 4. Tom Mitchell. Machine Learning. McGraw Hill, 1997. |
| 5. R. O. Duda et al., Pattern Classification. Wiley Interscience | |
| 6. Hastie, Tibshirani and Friedman. The Elements of Statistical Learning. Springer-Verlag, 2001. |
Evaluation System
| Requirements | Number | Percentage of Grade |
|---|---|---|
| Attendance/Participation | - | - |
| Laboratory | - | - |
| Application | - | - |
| Field Work | - | - |
| Special Course Internship | - | - |
| Quizzes/Studio Critics | - | - |
| Homework Assignments | - | - |
| Presentation | - | - |
| Project | 3 | 30 |
| Report | - | - |
| Seminar | - | - |
| Midterms Exams/Midterms Jury | 1 | 35 |
| Final Exam/Final Jury | 1 | 35 |
| Toplam | 5 | 100 |
| Percentage of Semester Work | 65 |
|---|---|
| Percentage of Final Work | 35 |
| Total | 100 |
Course Category
| Core Courses | |
|---|---|
| Major Area Courses | |
| Supportive Courses | X |
| Media and Managment Skills Courses | |
| Transferable Skill Courses |
The Relation Between Course Learning Competencies and Program Qualifications
| # | Program Qualifications / Competencies | Level of Contribution | ||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
| 1 | To become familiar with the state-of-the art and the literature in the software engineering research domain | X | ||||
| 2 | An ability to conduct world-class research in software engineering and publish scholarly articles in top conferences and journals in the area | |||||
| 3 | Be able to conduct quantitative and qualitative studies in software engineering | X | ||||
| 4 | Acquire skills needed to bridge software engineering academia and industry and to develop and apply scientific software engineering approaches to solve real-world problems | X | ||||
| 5 | An ability to access information in order to follow recent developments in science and technology and to perform scientific research or implement a project in the software engineering domain. | |||||
| 6 | An understanding of professional, legal, ethical and social issues and responsibilities related to Software Engineering. | |||||
| 7 | Skills in project and risk management, awareness about importance of entrepreneurship, innovation and long-term development, and recognition of international standards of excellence for software engineering practices standards and methodologies. | |||||
| 8 | An understanding about the impact of Software Engineering solutions in a global, environmental, societal and legal context while making decisions. | X | ||||
| 9 | Promote the development, adoption and sustained use of standards of excellence for software engineering practices. | |||||
ECTS/Workload Table
| Activities | Number | Duration (Hours) | Total Workload |
|---|---|---|---|
| Course Hours (Including Exam Week: 16 x Total Hours) | |||
| Laboratory | |||
| Application | |||
| Special Course Internship | |||
| Field Work | |||
| Study Hours Out of Class | 16 | 2 | 32 |
| Presentation/Seminar Prepration | |||
| Project | 3 | 5 | 15 |
| Report | |||
| Homework Assignments | |||
| Quizzes/Studio Critics | |||
| Prepration of Midterm Exams/Midterm Jury | 1 | 10 | 10 |
| Prepration of Final Exams/Final Jury | 1 | 20 | 20 |
| Total Workload | 77 | ||