COMPUTING SCIENCE
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University | Computing Science | Modules | CSCU9T6 | Useful Resources Updated 12/03/12 18:35
CSCU9T6 Spring 2017

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CSCU9T6 Useful Resources

Data Mining

For a good introduction to data mining, read this booklet from the American data mining company, Two Crows.
Down load the pdf

We will be using a free software package called Weka for the practical work and the assignment.
Download Weka Software

There are several data mining books in the library. These ones are worth a look.

  • Data Mining: Practical Machine Learning Tools and Techniques by I.H. Witten and E. Frank
  • Berry , M.J.A., Linoff, G.S. (1997 1 st ed/2004 April 2 nd ed) Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management , Wiley
  • Data mining : concepts and techniques / Jiawei Han [and] Micheline Kamber. San Francisco ; London : Morgan Kaufmann Publishers, c2001
There is a statistical element to this course. If you are uneasy with statistics, work through this simple tutorial. Stick to level one only and concentrate on the topics covering histograms, scatter plots, central tendency and correlation.
Go to the statistics tutorial

Bayesian Networks

Before you start this part of the course, make sure you understand the basic concepts of probability. There will be a lecture on the subject, but you will find it all easier if you already know how to calculate the probability of a certain event. If you cannot quickly answer the following questions, then you need to do a bit of preparation:
  • What is the probability of picking a red card from a deck of 52 playing cards?
  • What is the probability of picking a spade from a deck of 52 playing cards?
  • What is the probability of picking an ace from a deck of 52 playing cards?
  • What is the probability of picking a red ace from a deck of 52 playing cards?
  • What is the probability of picking two red cards in a row from a deck of 52 playing cards?
If you can't answer the questions above, a quick visit to this web site should help.

There are several copies of the textbook for this course in the RBR.
Korb, K.B., Nicholson, A.E. (2004) Bayesian Artificial Intelligence, Chapman & Hall/CRC

Chris Bishop has a very good (if a little challenging) book called Pattern Recognition and Machine Learning. You can see some things about it here:

http://research.microsoft.com/en-us/um/people/cmbishop/PRML/index.htm

The sample chapter is about Bayesian networks, amongst other things.

We will be using a free software package called Netica for the practical work
Download Netica Software

     
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