Machine Learning (934G5)

15 credits, Level 7 (Masters)

Spring teaching

On this module, you explore advanced techniques in machine learning. You use a systematic treatment, based on the following three key ingredients:

  • tasks
  • models
  • features.

As part of the module, you are introduced to both regression and classification, and your studies emphasise concepts such as model performance, learnability and computational complexity.

You learn techniques including:

  • probabilistic and non-probabilistic classification and regression methods
  • reinforcement learning approaches including the non-linear variants using kernel methods.

You are also introduced to techniques for pre-processing the data (including PCA). You will learn to implement, develop and deploy these techniques to real-world problems.

In order to take this module, you need to have already taken a relevant mathematical module or have equivalent prior experience.


67%: Lecture
33%: 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 2023/24. 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: