2024/25 Taught Postgraduate Module Catalogue

OCOM5200M Machine Learning

15 creditsClass Size: 100

Module manager: Dr Arash Rabbani
Email: A.Rabbani@leeds.ac.uk

Taught: 1 Sep to 31 Oct View Timetable

Year running 2024/25

Pre-requisite qualifications

A level maths, in particular:
- Basic understanding of statistics: mean, variance, Gaussian distribution, probability distribution
- Differentiation, integration

Pre-requisites

OCOM5100M

Programming 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.

Objectives

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

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

Teaching methods

Delivery type	Number	Length hours	Student hours
On-line Learning	6	1.00	6.00
Group learning	6	2.00	12.00
Independent online learning hours			28.00
Private study hours			104.00
Total Contact hours			18.00
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

Coursework

Assessment type	Notes	% of formal assessment
Assignment	Technical Report	70.00
In-course Assessment	In-course MCQ	30.00
Total percentage (Assessment Coursework)		100.00

This module will be reassessed by a 100% individual assessment.

Reading list

The reading list is available from the Library website

Last updated: 15/08/2024

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