Course title Number |
Data Mining 10681467 |
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Instructor(s) name(s) |
Dr. Fady DRaidi |
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Course Contents (description) |
Data Mining studies algorithms and computational paradigms that allow computers to find patterns and regularities in databases, perform prediction and forecasting, and generally improve their performance through interaction with data. It is currently regarded as the key element of a more general process called Knowledge Discovery that deals with extracting useful knowledge from raw data. The knowledge discovery process includes data selection, cleaning, coding, using different statistical and machine learning techniques, and visualization of the generated structures. The course will cover all these issues and will illustrate the whole process by examples |
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Course Objectives |
· Present fundamental concepts and techniques for data mining · Provide necessary background for applying data mining to business problems · Conduct case studies on real data mining examples · Practice data mining tools on real data |
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Intended learning Outcomes and Competences |
Upon successful completion of the course the student will: · Be able to understand the concepts, strategies, and methodologies related to the design and construction of data mining · Be able to comprehend several data preprocessing methods · Be able to utilize data warehouses and OLAP for data mining and knowledge discovery activities · Be able to determine an appropriate mining strategy for given large dataset · Be able to apply appropriate mining techniques to extract unexpected patterns and new rules that are "hidden" in large databases · Be able to obtain knowledge of current data mining applications |
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Textbook and |
Introduction to Data Mining, P.N. Tan, M.
Steinbach, V. Kumar |
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References |
· Jiawei Han, Micheline Kamber and Jian Pei, Data Mining: Concepts and Techniques, 3rd ed · E. Alpaydin. Introduction to Machine Learning, 2nd ed., MIT Press, 2011. · T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed., Springer-Verlag, 2009. · T. M. Mitchell, Machine Learning, McGraw Hill, 1997. · P.-N. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining, Addison Wesley, 2005. · I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann, 2nd ed
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Assessment Criteria |
Activity |
Date |
Percent (%) |
First Exam |
20 |
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Second Exam |
20 |
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Homework and quizzes |
20 |
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Final Exam |
40 |
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Subject |
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1 |
Introduction |
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2 |
Data and Data Exploration |
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3 |
Classification Algorithms |
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4 |
Association Analysis |
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5 |
Clustering |
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7 |
Anomaly Detection |
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9 |
Special lectures |