This course aims to introduce the fundamental issues of data mining, to provide students with a broad understanding of the algorithms developed to address different data mining tasks and apply them to real-world problems.
Theoretical lectures, laboratory exercises, projects, readings (journal and conference papers) and discussions.
Principle Sources
Introduction to Data Mining, Pang-Ning Tan, Michigan State University, Michael Steinbach, University of Minnesota, Vipin Kumar, University of Minnesota, Publisher: Pearson, 2005 (First Edition).
o https://www-users.cs.umn.edu/~kumar001/dmbook/firsted.php
Introduction to Data Mining, Pang-Ning Tan, Michigan State University, Michael Steinbach, University of Minnesota, Vipin Kumar, University of Minnesota, Publisher: Pearson, 2018 (Second Edition).
o https://www-users.cs.umn.edu/~kumar001/dmbook/index.php
Other Sources
Data mining: Concepts and Techniques, Jiawei Han, Micheline Kamber, Jian Pei, Publisher: Elsevier Morgan Kaufmann 2012 (Third Edition).
Pattern Classification, Richard O. Duda, Peter E. Hart, David G. Stork, Publisher: Wiley-Interscience Publication, 2001 (Second Edition).
Machine Learning, Tom M. Mitchell, Publisher: McGraw Hill, 1997.
Course Schedules
Week
Contents
Learning Methods
1. Week
Overview
Oral Presentation, Laboratory
2. Week
Introduction to Data Mining
Oral Presentation, Laboratory
3. Week
Data
Oral Presentation, Laboratory
4. Week
Data
Oral Presentation, Laboratory
5. Week
Data Exploration
Oral Presentation, Laboratory
6. Week
Data Exploration
Oral Presentation, Laboratory
7. Week
Classification
Oral Presentation, Laboratory
8. Week
Midterm Exam
9. Week
Classification
Oral Presentation, Laboratory
10. Week
Model Overfitting
Oral Presentation, Laboratory
11. Week
Model Evaluation
Oral Presentation, Laboratory
12. Week
Rule-Based Classifiers
Oral Presentation, Laboratory
13. Week
Rule-Based Classifiers
Oral Presentation, Laboratory
14. Week
Review for Final Exam
Oral Presentation, Laboratory
15. Week
16. Week
17. Week
Assessments
Evaluation tools
Quantity
Weight(%)
Midterm(s)
1
30
Quizzes
6
10
Project(s)
1
20
Final Exam
1
40
Program Outcomes
PO-1
Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline; ability to use theoretical and applied information in these areas to model and solve engineering problems.
PO-2
Ability to identify, formulate, and solve complex engineering problems; ability to select and apply proper analysis and modelling methods for this purpose.
PO-3
Ability to design a complex system, process, device or product under realistic constraints and conditions, in such a way so as to meet the desired result; ability to apply modern design methods for this purpose. (Realistic constraints and conditions may include factors such as economic and environmental issues, sustainability, manufacturability, ethics, health, safety issues, and social and political issues according to the nature of the design.)
PO-4
Ability to devise, select, and use modern techniques and tools needed for engineering practice; ability to employ information technologies effectively.
PO-5
Ability to design and conduct experiments, gather data, analyse and interpret results for investigating engineering problems.
PO-6
Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually.
PO-7
Ability to communicate effectively, both orally and in writing; knowledge of a minimum of one foreign language.
PO-8
Recognition of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself.
PO-9
Awareness of professional and ethical responsibility.
PO-10
Information about business life practices such as project management, risk management, and change management; awareness of entrepreneurship, innovation, and sustainable development.
PO-11
Knowledge about contemporary issues and the global and societal effects of engineering practices on health, environment, and safety; awareness of the legal consequences of engineering solutions.
Learning Outcomes
LO-1
Ability to understand, interpret and analyze the solutions of data mining applications and problems.
LO-2
Understanding how data mining methods work on different types of data.
LO-3
Implementing data mining algorithms.
LO-4
Ability to independently pursue a given data warehouse and data mining project by understanding the optimal algorithm for solving a particular problem.
