|Module Name||Multivariate Linear Analysis (MLA)|
|ECTS Weighting ||5 ECTS|
|Semester Taught||Semester 1|
|Module Coordinator/s||Arthur White|
Module Learning Outcomes
On successful completion of this module, students will 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.
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 Component||Brief Description||Learning Outcomes Addressed||% of Total||Week Set||Week Due|
|Examination||Take-home Exam (5 hours)||LO1, LO2, LO3||80%||N/A||N/A|
|Continuous Assessment||Mid-Term Assignment||LO1, LO2, LO3||20%||Week 6||Week 10|
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|
|Tutorial or seminar||0 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 Hours||116 hours|
Recommended Reading List
- C.M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
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.