Fundamentals of Machine Learning (G6061)

15 credits, Level 5

Spring teaching

In this module, you are introduced to the important field of machine learning.

You use a systematic approach, based on the following three key ingredients:

  • tasks
  • models
  • features.

You are introduced to both regression and classification, and your studies emphasise concepts such as model performance and learnability.

As part of this module, you learn techniques such as:

  • linear regression
  • single and multiple layer perceptron classification
  • kernel-based models (including RBF and SVM)
  • decision tree models and random forest
  • Naïve Bayes classification and k-means clustering.

You are also introduced to techniques for pre-processing the data (including PCA).

In this module, you adopt an example-based approach throughout.

Teaching and assessment

We’re currently reviewing teaching and assessment of our modules in light of the COVID-19 situation. We’ll publish the latest information as soon as possible.

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.

This module is running in the academic year 2021/22. We also plan to offer it in future academic years. However, we are constantly looking to improve and enhance our courses. There may be changes to modules in response to student demand or feedback, changes to staff expertise or updates to our curriculum. We may also need to make changes in response to COVID-19. We’ll make sure to let our applicants know of material changes to modules at the earliest opportunity.


This module is offered on the following courses: