Module 20233 (2012)

Module Description - Intelligent Data Analysis (Extended)

The Module Description is a strict subset of the Syllabus Page, which gives more information

Module TitleIntelligent Data Analysis (Extended)
SchoolComputer Science
Module Code06-20233
DescriptorCOMP/06-20233/LM
Member of StaffPeter Tino
LevelM
Credits10
Semester2
Pre-requisitesNone
Co-requisitesNone
RestrictionsMay not be taken with 06-20122 (Intelligent Data Analysis).
Contact hours24
Delivery2 hrs of lectures per week
Description
Outcomes
On successful completion of this module, the student should be able to:Assessed by:
explain principles and algorithms for dimensionality reduction and clustering of vectorial data Examination
explain principles and techniques for mining textual data Examination
demonstrate understanding of the principles of efficient web-mining algorithms Examination
demonstrate understanding of broader issues of learning and generalisation in pattern analysis and data mining systems Examination
identify, justify and apply suitable dimensionality reduction and visualisation techniques to mine real-world vectorial data Coursework
AssessmentSessional: 1.5 hr examination (80%), continuous assessment (20%).
Supplementary (where allowed): By examination only.
TextsTrevor Hastie, Robert Tibshirani, & Jerome Friedman , The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2001
David J. Hand, Heikki Mannila & Padhraic Smyth , Principles of Data Mining, 2003
Margaret H. Dunham, Data Mining: Introductory and Advanced Topics, 2003
Paolo Giudici, Applied Data Mining: Statistical Methods for Business and Industry, 2003