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

67%: Lecture
33%: Practical (Laboratory)

Assessment

100%: Coursework (Computer-based examination, 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 2021/22. However, there may be changes to these modules in response to COVID-19, staff availability, student demand or updates to our curriculum. We’ll make sure to let our applicants know of material changes to modules at the earliest opportunity.

Courses

This module is offered on the following courses: