Bishop, Christopher, 2006.Pattern Recognition and Machine Learning, Springer
Science,
Hastie Trevor, Tibshirani, R., Friedman, J. 2009, The Elements of Statistical
Learning, 2nd Edition, Springer Series,

None

The VLE (NETED)
The Internet
Hardware Laboratory
Appropriate Software
Word Processing software for use in the coursework
Printed and electronic journals.
Computer system and other devices' manuals

A contextualised worked assignment and file of evidence of 1500 words weighted at 100%.

Assignment (Learning outcomes1,2, 3 and 4)

To pass this module student must obtain 40% marks. Re-assessment is capped at 40%

The learning strategy for the module requires students to commit 150 learning hours, of this there will be 38 hours of class support and 112 hours of independent and self directed study.

Lectures/ lab work/ Presentations /Tutorial
Student managed learning/ directed learning

Apart from the lecture each week, various other methods as mentioned above will be used as learning strategies.
There will be laboratory and tutorial sessions, students will be required to discuss and present various topics of the module in the class. Students will be required to apply theoretical knowledge in practical contexts.

Topics will be drawn from

- Introduction
- Overview
- Handling Missing data
- Data Mining Techniques
- classification, link analysis/association rule mining,
- clustering,
- probabilistic/statistical methods,
- genetic algorithms,
- neural networks
- visualisation techniques
- Data Mining and Knowledge Discovery Tools and applications
- Data mining applications
- Customer Modelling/Profiling, Fraud Detection, Marketing, e-Commerce, market basket Analysis
- Criminal Analysis, Cybercrime, terrorism behaviour detection