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 (Report)

Contact hours and workload

This module is 150 hours of work. This breaks down into 33 hours of contact time and 117 hours of independent study.

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.