# STU33011 – Multivariate Linear Analysis

**Source**: https://teaching.scss.tcd.ie/module/stu33011-multivariate-linear-analysis/
**Parent**: https://www.maths.tcd.ie/undergraduate/modules/minor-stats.php

|  |  |
| --- | --- |
| **Module Code** | STU33011 |
| **Module Name** | Multivariate Linear Analysis (MLA) |
| **ECTS Weighting [**[1]**](https://teaching.scss.tcd.ie/wp-admin/post.php?post=375&action=edit#_ftn1)** | 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:

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 Component** | **Brief Description** | **Learning Outcomes Addressed** | **% of Total** | **Week Set** | **Week Due** |
| Examination | In Person (2 hours) | LO1, LO2, LO3 | 80% | N/A | N/A |
| Continuous Assessment | Mid-Term Assignment | LO1, LO2, LO3 | 10% | Week 6 | Week 8 |
| Continuous Assessment | Group project | LO1, LO2, LO3 | 10% | Week 10 | Week 12 |

## Reassessment Details

In person (2 hours).

## Contact Hours and Indicative Student Workload

|  |  |
| --- | --- |
| **Contact Hours (scheduled hours per student over full module), broken down by**: | **33 hours** |
| Lecture | 22 hours |
| Laboratory | 11 hours |
| Tutorial or seminar | 0 hours |
| Other | 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.

## Module Pre-requisites

**Prerequisite modules:** STU23501

**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

N/A

## Module Website

[STU33011 Website](https://scss.tcd.ie/~arwhite/Teaching/STU33011.html)

[Blackboard](https://tcd.blackboard.com/webapps/login/)