COMPUTING SCIENCE
AND MATHEMATICS

University of Stirling Logo
University | Computing Science | Modules | CSCU9T6 | Home Updated 10/05/16 11:05
CSCU9T6 Spring 2017

This course is not currently running, wait for this message to vanish before using these pages.

Home

Organisation

Materials

Assessment

Reference

 

CSCU9T6 Syllabus

Credits


Warning: file_get_contents(): HTTP request failed! HTTP/1.1 400 Bad Request in /home/courses/CSCU9T6/www/syllabus.php on line 18

Warning: file_get_contents(http://quarter.cs.stir.ac.uk/~kms/courseadmin/info.php?do=prereqs&course=CSCU9T6): failed to open stream: No such file or directory in /home/courses/CSCU9T6/www/syllabus.php on line 18

Learning Outcomes

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

Transferable Skills

  • 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

Contents

Bayesian Belief Networks

  • Understanding the use of probability information in predicting data values
  • Development of Bayesian belief network models
  • Hidden Markov models

Data Mining

  • 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

Reasoning Systems

  • Data, information, knowledge
  • Rule-based systems
  • Uncertainty: fuzzy logic, certainty factors
  • Case based reasoning
Students should also be able to demonstrate the ability to apply theory and techniques to unseen problems without reference to notes, to work independently and under a time constraint.

Assessment


Warning: file_get_contents(): HTTP request failed! HTTP/1.1 400 Bad Request in /home/courses/CSCU9T6/www/syllabus.php on line 70

Warning: file_get_contents(http://quarter.cs.stir.ac.uk/~kms/courseadmin/info.php?do=printassgs&course=CSCU9T6): failed to open stream: No such file or directory in /home/courses/CSCU9T6/www/syllabus.php on line 70

Textbooks

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)

Requirements

Attendance

Plagiarism

Handbook

     
Coordinator , Room
Email - Tel 01786 46 - Fax 01786 464551
Mail Computing Science and Mathematics, University of Stirling, Stirling, Scotland, FK9 4LA
Contact Details