| School/Faculty/Institute |
Graduate School |
| Course Code |
CSE 604 |
| Course Title in English |
Advanced Data Mining Principles |
| Course Title in Turkish |
İleri Veri Madenciliği İlkeleri |
| Language of Instruction |
EN |
| Type of Course |
Flipped Classroom |
| Level of Course |
Advanced |
| Semester |
Spring |
| Contact Hours per Week |
| Lecture: 3 |
Recitation: 0 |
Lab: 0 |
Other: 0 |
|
| Estimated Student Workload |
188 hours per semester |
| Number of Credits |
7.5 ECTS |
| Grading Mode |
Standard Letter Grade
|
| Pre-requisites |
None |
| Expected Prior Knowledge |
None |
| Co-requisites |
None |
| Registration Restrictions |
Only Doctorate Students |
| Overall Educational Objective |
To learn advanced data mining processing methods and how to design and implement intelligent systems to predict and model the big data sets. |
| Course Description |
This course introduces data mining concepts. Basic concepts in data mining: frequent item set detection, association rules, clustering and classification and regression decision trees, logistic models, and neural network models are covered in depth. In addition, students will learn how to compare analytical results and give recommendations during the data mining process. |
| Course Description in Turkish |
Bu ders veri madenciliği kavramlarına girişi sağlar. Veri madenciliğinde temel kavramlar: sık madde kümesi tespiti, ilişkilendirme kuralları, kümeleme ve sınıflandırma ve regresyon karar ağaçları, lojistik modeller ve sinir ağı modelleri derinlemesine ele alınmıştır. Ayrıca, öğrenciler analitik sonuçların nasıl karşılaştırılacağını öğrenecek ve veri madenciliği sürecinde tavsiyelerde bulunacaklar. |
Course Learning Outcomes and Competences
Upon successful completion of the course, the learner is expected to be able to:
1) Apply big data processing methodologies to investigate patterns in data set
2) Apply advanced classification and clustering methods to predict future trend
3) Assess the performance of the data mining methods
4) Design and construct hybrid data mining methods for a business data set
5) Communicate effectively by means of reports and presentations
6) Analyze and interpret data, and use analytic thinking to draw conclusions
7) Acquire and apply new knowledge as needed
|
| Program Learning Outcomes/Course Learning Outcomes |
1 |
2 |
3 |
4 |
5 |
6 |
7 |
Relation to Program Outcomes and Competences
| N None |
S Supportive |
H Highly Related |
| |
|
|
| |
Program Outcomes and Competences |
Level |
Assessed by |
| Prepared by and Date |
, |
| Course Coordinator |
İLKER BEKMEZCİ |
| Semester |
Spring |
| Name of Instructor |
Prof. Dr. İLKER BEKMEZCİ |
Course Contents
| Week |
Subject |
| 1) |
Measurement and Data |
| 2) |
Data Warehouse and OLAP Technology for Data Mining |
| 3) |
Data Processing |
| 4) |
Data Mining Primitives and Languages |
| 5) |
Concept Description: Characterization and Comparison |
| 6) |
Mining Association Rules in Large Databases |
| 7) |
Association Mining to Correlation Analysis |
| 8) |
Classification and Prediction |
| 9) |
Cluster Analysis |
| 10) |
Mining Complex Type of Data |
| 11) |
Mining Spatial Databases |
| 12) |
Mining Multimedia Databases |
| 13) |
Mining Semi Structured Data |
| 14) |
Mining Non-structed Data |
| 15) |
Final Examination Period |
| 16) |
Final Examination Period |
| Required/Recommended Readings | Han, J., Kamber M.;Data Mining: Concepts and Techniques
Pyle, D.J; Data Preparation for Data Mining
David Hand, Heikki Mannila and Padhraic Smyth, Principles of Data Mining, MIT Press, 2001
|
| Teaching Methods | Flipped learning is used as an instructional strategy. Students work individually for applied assignments.
|
| Homework and Projects | Assignments |
| Laboratory Work | None |
| Computer Use | Required |
| Other Activities | None |
| Assessment Methods |
| Assessment Tools |
Count |
Weight |
| Homework Assignments |
1 |
% 25 |
| Project |
1 |
% 25 |
| Midterm(s) |
1 |
% 50 |
| TOTAL |
% 100 |
|
| Course Administration |
karahocaa@mef.edu.tr
02123953600
Academic dishonesty and plagiarism will be subject to Law on Higher Education Article 54. |
Information Package / Course Catalogue