Prerequisites for Module
None except for course entry requirements.
Corequisite Modules
None.
Precluded Modules
None.
Aims of Module
To provide an understanding of the main principles underlying Data Mining applied to real-world datasets. To also provide specialised knowledge and valuable insights into algorithms that are at the forefront of machine learning research.
Learning Outcomes for Module
On completion of this module, students are expected to be able to:
| 1. |
Discuss, compare and contrast the advantages and disadvantages of applying a specific data mining technique to a given learning task.
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| 2. |
Use a toolkit to develop a data mining application tailored to a given learning task.
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| 3. |
Effectively interpret the results of learning through an understanding of the strengths and limitations of data mining technology and the selection of an appropriate evaluation technique.
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| 4. |
Demonstrate knowledge of the state-of-the-art in data mining and an awareness of current areas of research.
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| 5. |
Apply and, where necessary, adapt an appropriate data mining technique to a given problem.
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Indicative Module Content
Basic data mining concepts. Implementation of fundamental learning approaches and attribute selection methods.
Rules involving relations; incorporating domain knowledge in learning. State-of-the-art algorithms such as random forests, SVM and deep-learning principals including meta-learners.
| Advanced techniques for evaluating learned concepts.
Comparative studies for Data Mining algorithms.
Indicative Student Workload
Contact Hours
| Part Time | Laboratories
| 24 | Lectures
| 24 |
Directed Study
| | Assessment
| 7 | Directed Study
| 45 |
Private Study
| | Private Study
| 50 |
Mode of Delivery
This is a lecture based course, supplemented with laboratory sessions, where a data mining toolkit is applied to varied learning tasks and tutorials where additional understanding is gained through practical exercises which supplement the lectures.
Assessment Plan
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Learning Outcomes Assessed
| | Component 1 | 1,3,4,5
| | Component 2 | 2
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Component 2 - This is a practical examination worth 30% of the total module assessment.
Component 1 - This is a closed book examination worth 70% of the total module assessment.
Indicative Bibliography
| 1. | WITTEN, I., FRANK, E. and HALL M., 2011. Data Mining: Practical Machine Learning Tools and Techniques 3rd Ed. Morgan Kaufmann.
| | 2. | HAN,J., KAMBER, M. and PEI, J., 2011. Data Mining: Concepts and Techniques 3rd Ed, Morgan Kaufmann.
| | 3. | MITCHELL, T., 1997. Machine Learning. McGraw-Hill.
| | 4. | LANTZ, B., 2013. Machine Learning with R. Packt Publishing.
| | 5. | ZHAO, Y., 2012. R and Data Mining: Examples and Case Studies. Academic Press.
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