School of Computer Science

Module 06-20233 (2013)

Intelligent Data Analysis (Extended)

Level 4/M

Peter Tino Semester 2 10 credits
Co-ordinator: Peter Tino
Reviewer: Russell Beale

The Module Description is a strict subset of this Syllabus Page.

Outline


Aims

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:

1 explain principles and algorithms for dimensionality reduction and clustering of vectorial data
2 explain principles and techniques for mining textual data
3 demonstrate understanding of the principles of efficient web-mining algorithms
4 demonstrate understanding of broader issues of learning and generalisation in pattern analysis and data mining systems
5 identify, justify and apply suitable dimensionality reduction and visualisation techniques to mine real-world vectorial data


Restrictions


Cannot be taken with


Teaching methods

2 hrs of lectures per week

Contact Hours: 23


Assessment

Sessional: 1.5 hr examination (80%), continuous assessment (20%)

Supplementary (where allowed): 1.5 hr examination (80%) with the continuous assessment mark carried forward (20%)


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


Programmes containing this module