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The student should know and understand:
- The importance of data in organisations, identifying the difference between data and information
- The concept of data mining
- Different techniques that can be used to mine data, with particular emphasis given to the use of Bayesian belief networks
- The role of graphics and visualisation in data mining and data representation
- How reasoning processes can be implemented, to extend stored knowledge
- Different types of information systems and the methods they adopt for knowledge discovery
- Understanding of how data is transformed into information
- Good knowledge of information systems in terms of technologies used and their applicability for different tasks
- Knowledge of key data mining techniques
Bayesian Belief Networks
- Understanding the use of probability information in predicting data values
- Development of Bayesian belief network models
- Hidden Markov models
- An introduction to data mining
- Market analysis and machine learning.
- Statistical and other techniques for data mining.
- Tailoring information systems.
- Importance of data visualization
- Data warehousing concepts
- Data, information, knowledge
- Rule-based systems
- Uncertainty: fuzzy logic, certainty factors
- Case based reasoning
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Data Mining: Practical Machine Learning Tools and Techniques, I.H. Witten and E. Frank. 2nd Edition. Morgan Kaufmann, 2005.
Data Mining Techniques: for Marketing, Sales, and Customer Relationship Management (1st ed 1997 or 2nd ed 2004), MJA Berry and GS Linoff, Wiley (background).
Bayesian Artificial Intelligence, KB Korb and AE Nicholson, Chapman and Hall/CRC, 2004 (background).
Artificial Intelligence: A Guide to Intelligent Systems, 3rd edition (2011), Michael Negnevitsky, ISBN: 1408225743, Addison Wesley (background reading)