Fundamentals of Machine Learning (G6061)
15 credits, Level 5
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:
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.
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.
This module is running in the academic year 2020/21. 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:
- Computer Science (with an industrial placement year) BSc
- Computer Science (with an industrial placement year) MComp
- Computer Science BSc
- Computer Science MComp
- Computer Science and Artificial Intelligence (with an industrial placement year) BSc
- Computer Science and Artificial Intelligence BSc
- Finance and Technology (with a professional placement year) BSc
- Finance and Technology BSc