Machine Learning (934G5)

15 credits, Level 7 (Masters)

Spring teaching

In 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 then need to be able to implement, develop and deploy these techniques to real-world problems.

In order to take this module, you need to have already taken the 'Mathematics & Computational Methods for Complex Systems' module (817G5), or have taken an equivalent mathematical module or have equivalent prior experience.


67%: Lecture
33%: Practical (Laboratory)


100%: Coursework (Report)

Contact hours and workload

We’re currently reviewing contact hours for modules and will update with further information as soon as it is available.

This module is running in the academic year 2019/20. We also plan to offer it in future academic years. It may become unavailable due to staff availability, student demand or updates to our curriculum. We’ll make sure to let our applicants know of such changes to modules at the earliest opportunity.


This module is offered on the following courses: