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2021/22 Taught Postgraduate Module Catalogue

OCOM5200M Machine Learning

15 creditsClass Size: 100

Module manager: Marc de Kamps

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

Year running 2021/22

Pre-requisite qualifications

A level maths, in particular

Basic understanding of statistics: mean, variance, Gaussian distribution, probability distribution

Differentiation, integration


OCOM5100MProgramming for Data Science

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.


During this module:

Students will learn what are currently the most important algorithms that are available in machine learning.

Some knowledge of statistics will be refreshed and extended in order to help explain the theoretical underpinnings of these algorithms.

Students will learn about the relative strengths and weaknesses of the different algorithms.

Students will apply the algorithms in the exploration of data sets and the construction of models that help interpret the data.

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


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 ML. Examples will be drawn from simple problems that arise in robotics and data analytics.

Teaching methods

Delivery typeNumberLength hoursStudent hours
On-line Learning61.006.00
Group learning62.0012.00
Independent online learning hours28.00
Private study hours104.00
Total Contact hours18.00
Total hours (100hr per 10 credits)150.00

Private study

Private study will include directed reading and exercises and self-directed research in support of learning activities, as well as in preparation for assessments.

Independent online learning involves non-facilitated directed learning. Students will work through bespoke interactive learning resources and activities in the VLE.

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 self and peer assessment providing opportunities for formative feedback from peers and tutors. 

Methods of assessment

Assessment typeNotes% of formal assessment
In-course AssessmentReport60.00
In-course AssessmentOnline test20.00
In-course AssessmentOnline test20.00
Total percentage (Assessment Coursework)100.00

This module will be reassessed by an online time-constrained assessment

Reading list

The reading list is available from the Library website

Last updated: 30/06/2021 16:24:06


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