2024/25 Taught Postgraduate Module Catalogue

MATH5305M Learning from Data

10 creditsClass Size: 100

Module manager: Dr Georgios Aivaliotis
Email: G.Aivaliotis@leeds.ac.uk

Taught: Semester 1 (Sep to Jan) View Timetable

Year running 2024/25

Co-requisites

MATH5315M

Applied Statistics and Probability

This module is mutually exclusive with

MATH3772	Multivariate Analysis
MATH5772M	Multivariate&Cluster Analysis

This module is not approved as an Elective

Module summary

- introduction to statistical learning (supervised and unsupervised), classification and regression.- cluster analysis, clustering, K-means method, distances between/within clusters; MDS.- tree models, random forest and boosting.- regression models; linear regression and parameter estimation.- logistic regression; additive models.- dimension reduction; principal component and factor analysis; loading interpretation.

Objectives

Data surround us and are produced by any activity we do. In the financial sector, decisions need to be informed and supported by data. Modern techniques of data analytics using both statistical and machine learning methods form an essential toolkit for the data driven financier. This module will offer all the theoretical background and hands on applications using real data. Applications will include, propensity modelling, credit scoring, portfolio optimisation and more.

Learning outcomes
On completion of this module, the students will be able to
1. apply both classical statistical as well as machine learning methodologies for classification and regression problems
2. perform dimension reduction
3. apply clustering methods
4. exemplify the above on real, financial data

Syllabus

Teaching methods

Delivery type	Number	Length hours	Student hours
Lecture	22	1.00	22.00
Practical	5	1.00	5.00
Private study hours			73.00
Total Contact hours			27.00
Total hours (100hr per 10 credits)			100.00

Private study

Reading, problem solving, report writing, revising and computing.

Opportunities for Formative Feedback

Feedback on computer assignments and homeworks (3).

Methods of assessment

Coursework

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

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

Exams

Exam type	Exam duration	% of formal assessment
Open Book exam	2 hr	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

There is no reading list for this module

Last updated: 29/04/2024 16:16:34

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