Module 20233 (2013)
Syllabus page 2013/2014
06-20233
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
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: | 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 |
| 5 | identify, justify and apply suitable dimensionality reduction and visualisation techniques to mine real-world vectorial data | Continuous Assessment |
Restrictions, Prerequisites and Corequisites
Restrictions:
May not be taken with 06-20122 (Intelligent Data Analysis).
Prerequisites:
None
Co-requisites:
None
Teaching
Teaching Methods:
2 hrs of lectures per week
Contact Hours:
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%)
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: 22 August 2013
Source file: /internal/modules/COMSCI/2013/xml/20233.xml
Links | Outline | Aims | Outcomes | Prerequisites | Teaching | Assessment | Books | Detailed Syllabus