Module 20122 (2010)

Syllabus page 2010/2011

06-20122
Intelligent Data Analysis

Level 3/H

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


Outline

The module introduces a range of state-of-the-art techniques in the fields of statistical pattern analysis and data mining. The 'information revolution' has generated large amounts of data, but valuable information is often hidden and hence unusable. Pattern analysis and data mining techniques seek to unveil hidden patterns in the data that can help us to refine web search, construct more robust spam filters, or uncover principal trends in the evolution of a variety of stock indexes.


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: 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

Restrictions, Prerequisites and Corequisites

Restrictions:

May not be taken with 06-20233 (Intelligent Data Analysis (Extended)).

Prerequisites:

None

Co-requisites:

None


Teaching

Teaching Methods:

2 hrs of lectures per week

Contact Hours:

24


Assessment

  • Sessional: 1.5 hr examination (100%).
  • Supplementary (where allowed): As the sessional assessment

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/2010/xml/20122.xml

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