2019/20 Taught Postgraduate Module Catalogue

MATH5745M Multivariate Methods

15 creditsClass Size: 45

Module manager: Dr Andrew Baczkowski
Email: A.J.Baczkowski@leeds.ac.uk

Taught: Semester 2 (Jan to Jun) View Timetable

Year running 2019/20

Pre-requisite qualifications

MATH5741M, or equivalent.

This module is mutually exclusive with

MATH3772	Multivariate Analysis
MATH5772M	Multivariate&Cluster Analysis

This module is not approved as an Elective

Module summary

In big data with multiple variables, it is vital to discover pattern and infer valuable information from the data. This module introduces basic techniques from multivariate statistics, with the aim to discover, describe and exploit dependencies between variables in complex datasets.

Objectives

To introduce basic techniques from multivariate statistics, with the aim to discover, describe and exploit dependencies between variables in complex datasets.

Learning outcomes
On completion of the module, the student should:
- be able to discover and exploit dependency between variables;
- be able to reduce the dimension of a dataset with dependent components, and interpret the results;
- be able to identify clusters in a given data set;
- be able to visualise similarities between observations in lower dimension.

Syllabus

- Introduction to multivariate analysis
- Statistical dependence, covariance matrix
- High dimensional problems, the "curse of dimensionality"
- Principal Component Analysis (PCA), dimension reduction
- Clustering, K-means method, distances between/within clusters
- Multidimensional Scaling (MDS)

Teaching methods

Delivery type	Number	Length hours	Student hours
Lecture	22	1.00	22.00
Practical	11	1.00	11.00
Independent online learning hours			22.00
Private study hours			95.00
Total Contact hours			33.00
Total hours (100hr per 10 credits)			150.00

Private study

The student will be expected to complete regular written worksheet assignments testing their understanding of theoretical course elements.

The student will learn to perform analysis using the software package R, which includes performing dimension reduction such as principal component analysis and factor analysis, clustering such as hierarchical and k-means clustering, multi-dimensional scaling, and learning different types of plots for presenting high-dimensional data into informative two-dimensional. Part of the assessment for the module consists of a practical, where the student will apply these techniques to a real-world data set.

Opportunities for Formative Feedback

Monitoring by regular worksheets and achievement in supervised practical sessions.

Methods of assessment

Coursework

Assessment type	Notes	% of formal assessment
Practical	Report	30.00
Total percentage (Assessment Coursework)		30.00

There is no resit available for the coursework components of this module. If the module is failed, the coursework mark will be carried forward and added to the resit exam mark with the same weighting as listed above.

Exams

Exam type	Exam duration	% of formal assessment
Unseen exam	2 hr 00 mins	70.00
Total percentage (Assessment Exams)		70.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Reading list

The reading list is available from the Library website

Last updated: 05/11/2019 08:50:06

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