The main objective of this course is to provide students with the theoretical background and practical experience necessary to the application of data mining techniques to real world problems.
Prerequisite(s)
IE3101 Introduction to Probability
Corequisite(s)
-
Special Requisite(s)
-
Instructor(s)
Professor Ayça Çakmak Pehlivanlı
Course Assistant(s)
-
Schedule
This course is not offered in this semester.
Office Hour(s)
This course is not offered in this semester.
Teaching Methods and Techniques
Lectures, Project, Discussion, Homeworks
Principle Sources
P.N. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining, Addison Wesley, Pearson Education, 2005.
Other Sources
C. Shmueli, N. R. Patel, P. C. Bruce, Data Mining for Business Intelligence: Concepts, Techniques and Applications in Microsoft Office Excel with XLMiner, Wiley-Interscience, 2007.
J. Han and M. Kamber, Data Mining - concepts and Techniques, 2nd Edition, Morgan Kaufmann, 2006.
R. J. Roiger and M. W. Geatz, Data Mining - A Tutorial Based Primer, Addison Wesley, Pearson Education, 2003.
Course Schedules
Week
Contents
Learning Methods
1. Week
Overview of the course (syllbus, teaching methods, sources, outcomes) Introduction and Motivation to Data Mining - DM tools
Presentation
2. Week
Data Mining: Definitions, Motivation, Concepts and Techniques
Data: Types of Data, Data Quality, Data Preprocessing, Measures of Similarity and Dissimilarity
Presentation
3. Week
Data Preprocessing: Data Sampling, Data Cleaning, Feature Selection and Dimensionality Reduction
Presentation, Practice 1
4. Week
Review of related and basic Statistics Supervised Learning - Prediction : Linear Regression and applications
Classification: Tree-based Methods & Rule-based Methods & Bayesian
Presentation
5. Week
Supervised Learning - Classifications: Basic Concepts, Decision Trees and applications
Unsupervised Learning - Clustering: K-Means algorithms and applications
Presentation, Practice 7
14. Week
Review
Presentation
15. Week
Final Exam
16. Week
Final Exam
17. Week
Final Exam
Assessments
Evaluation tools
Quantity
Weight(%)
Midterm(s)
1
35
Homework / Term Projects / Presentations
1
25
Final Exam
1
40
Program Outcomes
PO-1
Ability to apply theoretical and practical knowledge gained by Mathematics, Science and their engineering fields and ability to use their knowledge in solving complex engineering problems.
PO-2
Ability of determining, defining, formulating and solving complex engineering problems; for that purpose develop the ability of selecting and implementing suitable models and methods of analysis.
PO-3
Ability of designing a complex system, process, device or product under real world constraints and conditions serving certain needs; for this purpose ability of applying modern design techniques
PO-4
Ability of selecting and using the modern techniques and devices which are necessary for analyzing and solving complex problems in engineering implementations; ability of efficient usage of information technologies.
PO-5
Ability of designing experiments, conducting tests, collecting data and analyzing and interpreting the solutions to investigate of complex engineering problems or discipline-specific research topics.
PO-6
Ability of working efficiently in intra-disciplinary and multi-disciplinary teams; individual working ability and habits.
PO-7
Ability of verbal and written communication skills; and at least one foreign language skills, ability to write effective reports and understand written reports, ability to prepare design and production reports, ability to make impressive presentation, ability to give and receive clear and understandable instructions
PO-8
Awareness of importance of lifelong learning; ability to access data, to follow up the recent innovation in science and technology for continuous self-improvement.
PO-9
Conformity to ethical principles; knowledge about occupational and ethical responsibility, and standards used in engineering applications.
PO-10
Knowledge about work life implementations such as project management, risk management and change management; awareness about entrepreneurship and innovativeness; knowledge about sustainable development.
PO-11
Knowledge about effects of engineering applications on health, environment and security in global and social dimensions, and on the problems of the modern age in engineering; awareness about legal outcomes of engineering solutions.
Learning Outcomes
LO-1
Explain what data mining is and how data mining can be employed to solve real life problems
LO-2
Recognize whether a data mining solution is a feasible alternative for a specific problem
LO-3
Apply statistical and nonstatistical techniques to evaluate the results of a data mining session
LO-4
Recognize several data mining strategies and know when each strategy is appropriate, Explain and apply how several data mining techniques build models to solve problems
LO-5
Learn how to preprocess data before applying data mining techniques, Practice data mining techniques on different data sets using a software package, Evaluate the data mining performance
LO-6
Discuss ethical and professional issues in data mining, Recognize the usage and the limitations of data mining algorithms
