Module 20122 (2013)
Syllabus page 2013/2014
06-20122
Intelligent Data Analysis
Level 3/H
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
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: | |
| 1 | explain 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 |
| 4 | demonstrate 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:
Assessment
- Sessional: 1.5 hr examination (100%).
- Supplementary (where allowed): As the sessional assessment
Recommended Books
| Title | Author(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 Topics | Margaret H. Dunham | Prentice Hall, 2003 |
| Applied Data Mining: Statistical Methods for Business and Industry | Paolo Giudici | John Wiley & Sons, 2003 |
Detailed Syllabus
- Overview. Various forms of pattern analysis and data mining
- Basics of vector and metric spaces, probability theory and statistics
- Analysing vectorial data
- Dimensionality reduction techniques
- Clustering techniques
- Classification and regression techniques
- Analysing structured data
- Mining textual data
- Other structured data types
- Searching the web
Last updated: 28 Nov 2006
Source file: /internal/modules/COMSCI/2013/xml/20122.xml
Links | Outline | Aims | Outcomes | Prerequisites | Teaching | Assessment | Books | Detailed Syllabus