STU33011 – Multivariate Linear Analysis

Module CodeSTU33011
Module NameMultivariate Linear Analysis (MLA)
ECTS Weighting [1]5 ECTS
Semester TaughtSemester 1
Module Coordinator/s  Arthur White

Module Learning Outcomes

On successful completion of this module, students will be able to:

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

Module Content

Classical multivariate techniques of principal component analysis, clustering, discriminant analysis, k-nearest neighbours, and logistic regression are investigated. 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 Analytics.

Teaching and Learning Methods

Lectures and labs.

Assessment Details

Assessment ComponentBrief DescriptionLearning Outcomes Addressed% of TotalWeek SetWeek Due
ExaminationTake-home Exam (5 hours)LO1, LO2, LO380%N/AN/A
Continuous AssessmentMid-Term AssignmentLO1, LO2, LO320%Week 6Week 10

Reassessment Details

Take-home exam (24 hours).

Contact Hours and Indicative Student Workload

Contact Hours (scheduled hours per student over full module), broken down by:33 hours
Lecture22 hours
Laboratory11 hours
Tutorial or seminar0 hours
Other0 hours
Independent Study (outside scheduled contact hours), broken down by:83 hours
Preparation for classes and review of material (including preparation for examination, if applicable)42 hours
Completion of assessments (including examination, if applicable)41 hours
Total Hours116 hours

Recommended Reading List

  • C.M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.

Module Pre-requisites

Prerequisite modules: N/A

Other/alternative non-module prerequisites:

Knowledge of linear algebra, e.g., matrix notation, eigenvalues and eigenvectors. Some familiarity with regression models, and with the R programming language, will also be helpful.

Module Co-requisites


Module Website

STU33011 Website