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 | Elective Courses Taken From Other Departments |
| Course Level | Natural & Applied Sciences Master's Degree |
| 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 | X |
| Supportive Courses | |
| 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 be able to use mathematics, science and engineering knowledge in solving engineering problems related to information systems. | X | ||||
| 2 | Design and conduct experiments in the field of informatics, analyze and interpret the results of experiments. | X | ||||
| 3 | Designs an information system, component and process according to the specified requirements. | X | ||||
| 4 | Can work effectively in disciplinary and multidisciplinary teams. | |||||
| 5 | Identify, formulate and solve engineering problems in the field of informatics. | X | ||||
| 6 | Acts in accordance with professional ethical rules. | |||||
| 7 | Communicates effectively both orally and in writing. | |||||
| 8 | Gains awareness of the necessity of lifelong learning. | |||||
| 9 | Learn about contemporary issues. | X | ||||
| 10 | To be able to use modern engineering tools, techniques and skills required for engineering practice. | X | ||||
| 11 | Knows project management methods and recognizes international standards. | X | ||||
| 12 | Develop informatics-related engineering products and prototypes for real-life problems. | X | ||||
| 13 | Contributes to professional knowledge. | |||||
| 14 | Can do methodological scientific research. | |||||
| 15 | Produce, report and present a scientific work based on an original or existing body of knowledge. | |||||
| 16 | Can defend the original idea generated. | |||||
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 | ||