Module 20122 (2013)

Module Description - Intelligent Data Analysis

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

Module TitleIntelligent Data Analysis
SchoolComputer Science
Module Code06-20122
DescriptorCOMP/06-20122/LH
Member of StaffPeter Tino
LevelH
Credits10
Semester2
Pre-requisitesNone
Co-requisitesNone
RestrictionsMay not be taken with 06-20233 (Intelligent Data Analysis (Extended)).
Contact hours23
Delivery2 hrs of lectures per week
Description 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.
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
AssessmentSessional: 1.5 hr examination (100%).
Supplementary (where allowed): As the sessional assessment
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