Module 20233 (2009)

Syllabus page 2009/2010

Intelligent Data Analysis (Extended)

Level 4/M

Peter Tino
10 credits in Semester 2

Links | Outline | Aims | Outcomes | Prerequisites | Teaching | Assessment | Books | Detailed Syllabus

The Module Description is a strict subset of this Syllabus Page. (The University module description has not yet been checked against the School's.)

Relevant Links

Module Web Page



The aims of this module are to:

  • introduce some of the fundamental techniques and principles of statistical pattern analysis and data mining
  • investigate some common text and high-dimensional data mining algorithms and their applications
  • present the fields of data mining and pattern analysis in the larger context of learning systems

Learning Outcomes

On successful completion of this module, the student should be able to: Assessed by:
1explain principles and algorithms for dimensionality reduction and clustering of vectorial data Examination
2 explain principles and techniques for mining textual data Examination
3 demonstrate understanding of the principles of efficient web-mining algorithms Examination
4demonstrate understanding of broader issues of learning and generalisation in pattern analysis and data mining systems Examination
5identify, justify and apply suitable dimensionality reduction and visualisation techniques to mine real-world vectorial data Coursework

Restrictions, Prerequisites and Corequisites


May not be taken with 06-20122 (Intelligent Data Analysis).






Teaching Methods:

2 hrs of lectures per week

Contact Hours:



  • Sessional: 1.5 hr examination (80%), continuous assessment (20%).
  • Supplementary (where allowed): By examination only.

Recommended Books

TitleAuthor(s)Publisher, Date
The Elements of Statistical Learning: Data Mining, Inference, and Prediction Trevor Hastie, Robert Tibshirani, & Jerome Friedman Springer, 2001
Principles of Data Mining David J. Hand, Heikki Mannila & Padhraic Smyth MIT Press, 2003
Data Mining: Introductory and Advanced TopicsMargaret H. DunhamPrentice Hall, 2003
Applied Data Mining: Statistical Methods for Business and IndustryPaolo GiudiciJohn Wiley & Sons, 2003

Detailed Syllabus

  1. Overview. Various forms of pattern analysis and data mining
  2. Basics of vector and metric spaces, probability theory and statistics
  3. Analysing vectorial data
    • Dimensionality reduction techniques
    • Clustering techniques
    • Classification and regression techniques
  4. Analysing structured data
    • Mining textual data
    • Other structured data types
  5. Searching the web

Last updated: 28 Nov 2006

Source file: /internal/modules/COMSCI/2009/xml/20233.xml

Links | Outline | Aims | Outcomes | Prerequisites | Teaching | Assessment | Books | Detailed Syllabus