Applied Machine Learning (G6061)

15 credits, Level 5

Spring teaching

In this module, you'll learn how machine learning learning methods can be applied to practical problems in different domains including natural language processing and computer vision.

We will discuss aspects such as:

• how different types of data can be effectively pre-processed.
• the mappings between problems and machine learning tasks and loss functions.
• system design considerations for different problems.
• metrics for evaluating the efficacy of predictions

As we work through a range of real-world applications, we will describe a variety of unsupervised and supervised machine learning models including classical machine learning tools and modern deep learning techniques. You'll be introduced to software packages to enable you to design and implement your own systems.



50%: Lecture
50%: Practical (Laboratory)


100%: Coursework (Report)

Contact hours and workload

This module is approximately 150 hours of work. This breaks down into about 33 hours of contact time and about 117 hours of independent study. The University may make minor variations to the contact hours for operational reasons, including timetabling requirements.

We regularly review our modules to incorporate student feedback, staff expertise, as well as the latest research and teaching methodology. We’re planning to run these modules in the academic year 2024/25. However, there may be changes to these modules in response to feedback, staff availability, student demand or updates to our curriculum. We’ll make sure to let you know of any material changes to modules at the earliest opportunity.


This module is offered on the following courses: