# Untitled
**Source**: https://teaching.scss.tcd.ie/wp-json/wp/v2/module/365
**Parent**: https://teaching.scss.tcd.ie/module/stu33011-multivariate-linear-analysis/
{"id":365,"date":"2020-07-17T14:59:50","date\_gmt":"2020-07-17T14:59:50","guid":{"rendered":"https:\/\/teaching.scss.tcd.ie\/?post\_type=module&p=365"},"modified":"2025-09-19T13:44:44","modified\_gmt":"2025-09-19T12:44:44","slug":"stu33011-multivariate-linear-analysis","status":"publish","type":"module","link":"https:\/\/teaching.scss.tcd.ie\/module\/stu33011-multivariate-linear-analysis\/","title":{"rendered":"STU33011 – Multivariate Linear Analysis"},"content":{"rendered":"\n
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| **Module Code<\/strong><\/td> STU33011<\/td><\/tr>| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | **Module Name<\/strong><\/td> Multivariate Linear Analysis (MLA)<\/td><\/tr>| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | **ECTS Weighting [**[1]<\/strong><\/a><\/strong><\/td> 5 ECTS<\/td><\/tr>| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | **Semester Taught<\/strong><\/td> Semester 1<\/td><\/tr>| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | **Module Coordinator\/s <\/strong><\/td> Arthur White<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\nModule Learning Outcomes<\/h2>\n\n\n\n On successful completion of this module, students will be able to:<\/p>\n\n\n\n \n1. Define and describe various classical dimension reduction techniques for multivariate data;<\/li>\n\n\n\n- Implement clustering and\/or classification algorithms and assess and compare the results;<\/li>\n\n\n\n- Interpret output of data analysis performed by a computer statistics package. <\/li>\n<\/ol>\n\n\n\nModule Content<\/h2>\n\n\n\n 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.<\/p>\n\n\n\nTeaching and Learning Methods<\/h2>\n\n\n\n Lectures and labs.<\/p>\n\n\n\nAssessment Details<\/h2>\n\n\n\n | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | **Assessment Component<\/strong><\/td> **Brief Description<\/strong><\/td> **Learning Outcomes Addressed<\/strong><\/td> **% of Total<\/strong><\/td> **Week Set<\/strong><\/td> **Week Due<\/strong><\/td><\/tr>| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Examination<\/td> In Person (2 hours)<\/td> LO1, LO2, LO3<\/td> 80%<\/td> N\/A<\/td> N\/A<\/td><\/tr>| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Continuous Assessment<\/td> Mid-Term Assignment<\/td> LO1, LO2, LO3<\/td> 10%<\/td> Week 6<\/td> Week 8<\/td><\/tr>| | | | | | | | | | | | | | | | | | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Continuous Assessment<\/td> Group project<\/td> LO1, LO2, LO3<\/td> 10%<\/td> Week 10<\/td> Week 12<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\nReassessment Details<\/h2>\n\n\n\n In person (2 hours).<\/p>\n\n\n\nContact Hours and Indicative Student Workload<\/h2>\n\n\n\n | | | | | | | | | | | | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | **Contact Hours (scheduled hours per student over full module), broken down by<\/strong>:<\/td> **33 hours<\/strong><\/td><\/tr>| | | | | | | | | | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Lecture<\/td> 22 hours<\/td><\/tr>| | | | | | | | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Laboratory<\/td> 11 hours<\/td><\/tr>| | | | | | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Tutorial or seminar<\/td> 0 hours<\/td><\/tr>| | | | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Other<\/td> 0 hours<\/td><\/tr>| | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | | **Independent Study (outside scheduled contact hours), broken down by:<\/strong><\/td> **83 hours<\/strong><\/td><\/tr>| | | | | | | | --- | --- | --- | --- | --- | --- | | Preparation for classes and review of material (including preparation for examination, if applicable)<\/td> 42 hours<\/td><\/tr>| | | | | | --- | --- | --- | --- | | Completion of assessments (including examination, if applicable)<\/td> 41 hours<\/td><\/tr>| | | | --- | --- | | **Total Hours<\/strong><\/td> **116 hours<\/strong><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\nRecommended Reading List<\/h2>\n\n\n\n \n- C.M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.<\/li>\n<\/ul>\n\n\n\nModule Pre-requisites<\/h2>\n\n\n\n **Prerequisite modules:<\/strong> STU23501<\/p>\n\n\n\n **Other\/alternative non-module prerequisites:<\/strong><\/p>\n\n\n\n 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.<\/p>\n\n\n\nModule Co-requisites<\/h2>\n\n\n\n N\/A<\/p>\n\n\n\nModule Website<\/h2>\n\n\n\n [STU33011 Website<\/a><\/p>\n\n\n\n [Blackboard<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" (Semester 1, 5 ECTS) Classical multivariate techniques of principal component analysis, clustering, discriminant analysis, k-nearest neighbours, and logistic regression are investigated.<\/p>\n","protected":false},"author":41,"menu\_order":0,"template":"","meta":[],"module\_category":[99,73,119,78],"class\_list":["post-365","module","type-module","status-publish","hentry","module\_category-msiss-year-3-core","module\_category-s1","module\_category-stats-year-3","module\_category-visiting"],"\_links":{"self":[{"href":"https:\/\/teaching.scss.tcd.ie\/wp-json\/wp\/v2\/module\/365","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/teaching.scss.tcd.ie\/wp-json\/wp\/v2\/module"}],"about":[{"href":"https:\/\/teaching.scss.tcd.ie\/wp-json\/wp\/v2\/types\/module"}],"author":[{"embeddable":true,"href":"https:\/\/teaching.scss.tcd.ie\/wp-json\/wp\/v2\/users\/41"}],"wp:attachment":[{"href":"https:\/\/teaching.scss.tcd.ie\/wp-json\/wp\/v2\/media?parent=365"}],"wp:term":[{"taxonomy":"module\_category","embeddable":true,"href":"https:\/\/teaching.scss.tcd.ie\/wp-json\/wp\/v2\/module\_category?post=365"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}](https://teaching.scss.tcd.ie/wp-json/wp/v2/module/\"https:\/\/tcd.blackboard.com\/webapps\/login\/\")](https://teaching.scss.tcd.ie/wp-json/wp/v2/module/\"https:\/\/scss.tcd.ie\/~arwhite\/Teaching\/STU33011.html\")****** |** | | | | |** |** | | | | | | | | |** |** | | | | | | | | | | | | | | | | | | |** |** |** |** |** |** | |** | |** | |**](https://teaching.scss.tcd.ie/wp-json/wp/v2/module/\"https:\/\/teaching.scss.tcd.ie\/wp-admin\/post.php?post=375&action=edit#_ftn1\")** | |** | |** |