2024/25 Taught Postgraduate Module Catalogue

OMAT5200M Machine Learning

15 creditsClass Size: 100

Module manager: Dr Hassan Izanloo
Email: H.Izanloo@leeds.ac.uk

Taught: 1 Sep to 31 Oct, 1 Sep to 31 Oct (adv yr) View Timetable

Year running 2024/25

Pre-requisite qualifications

None

Pre-requisites

OMAT5100M

Programming for Data Science

Module replaces

N/A

This module is not approved as an Elective

Module summary

Machine learning is a rapidly developing research area which takes an algorithmic approach to identifying patterns and statistical regularities in data without or with limited human intervention, often with the aim of supporting decision making. In this module you will learn to apply a number of machine learning techniques that are widely used in industry, government, and other large organisations. You will learn how the different approaches relate to and are motivated by statistics and will gain practical experience in the application of these techniques on real and simulated datasets.

Objectives

The module aims to give students the skills and experience to produce simple computer-based applications for a range of sectors based on widely used machine learning techniques such as linear regression, neural networks and decision trees. It prepares students to develop and integrate systems using data analysis techniques.

Learning outcomes
On completion of this module students should be able to:

1. State the main algorithms that are used in machine learning
2. Explain the ideas underlying these algorithms
3. Explain the relative strength and weaknesses of these algorithms
4. Implement these methods, or use existing implementations to explore data sets and build models
5. Evaluate the performance of the different algorithms

Skills outcomes
Skills developed in this module include:

- interpreting data and making decisions based on that interpretation.
- programming and use of statistical software.

Syllabus

Indicative content for this module includes:

Neural networks, decision trees, support vector machines, Bayesian learning, instance-based learning, linear regression, clustering, reinforcement learning, recent developments in machine learning. Examples will be drawn from simple problems that arise in data analytics and related areas.

Teaching methods

Delivery type	Number	Length hours	Student hours
On-line Learning	5	1.00	5.00
Discussion forum	6	2.00	12.00
Seminar	1	1.50	1.50
Independent online learning hours			42.00
Private study hours			89.50
Total Contact hours			18.50
Total hours (100hr per 10 credits)			150.00

Opportunities for Formative Feedback

Online learning materials will provide regular opportunity for students to check their understanding (for example through formative MCQs with automated feedback). Regular group activity embedded into learning will allow opportunities for formative feedback from peers and tutors.

Methods of assessment

Coursework

Assessment type	Notes	% of formal assessment
In-course Assessment	In-course MCQ	20.00
Assignment	Report	80.00
Total percentage (Assessment Coursework)		100.00

Students will resit by completing the Assignment in conjunction with a portion of the in-course assessment six months after the delivery of the module.

Reading list

There is no reading list for this module

Last updated: 08/08/2024

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