Skip to main content

Trinity College Dublin, The University of Dublin

Menu Search



Module Descriptor School of Computer Science and Statistics

Module CodeST3011
Module NameMLA
Module Short TitleMultivariate Linear Analysis (MLA)
ECTS5
Semester Taught1
Contact Hours

Lecture hours: 22, Lab hours: 11, Tutorial hours: 0 Total hours:33 

Module PersonnelDr. Brett Houlding   
Learning Outcomes

When students have successfully completed Multivariate Analysis they should be able to:  

  • Define and describe various classical dimension reduction techniques for multivariate data.
  • Implement clustering and/or classification algorithms and assess and compare the results.
  • Interpret output of data analysis performed by a computer statistics package.    
Learning Aims

Classical multivariate techniques of discriminant analysis, principal component analysis, clustering and logistic regression are examined. There is a strong emphasis on the use and interpretation of these techniques.  More modern techniques, some of which address the same issues, are covered in the SS module Data Mining.   

Module Content
  • Multivariate Analaysis      
  • Principal Components Analysis
  • Multidimensional Scaling
  • Factor Analysis
  • Hierarchical and Iterative Clustering
  • K-Nearest Neighbours
  • Discriminant Analysis
  • Logistic Regression  
Recommended Reading List

Multivariate Analysis:   Introduction to Multivariate Analysis, C. Chatfield and A. Collins, Chapman & Hall  

Module Prerequisites
Assessment Details

Exam: 80%     (2 hours)Coursework: 20%. 

Module Websitehttps://www.scss.tcd.ie/Brett.Houlding/Index/ST3011.html
Academic Year of Data2017/18