Machine Learning (prototype)
These courses aim to provide both beginners and more experienced users with the skills and knowledge required to implement Machine Learning techniques into their research. Whilst some theoretical knowledge will be covered these courses are predominantly focussed on providing practical solutions to research.
Please note that these courses are designed to support participants with a range of background knowledge and skills and as such some of the courses will only be suitable for individuals with some existing understanding and awareness of machine learning topics. All courses will have their pre-requisites clearly set out.
We are actively developing an applied Machine Learning curriculum, specifically targeted at a broader scientific audience beyond mathematicians, physicists and engineers and our courses are under regular review. If you have suggestions for machine learning topics that you would like delivered, please complete this form.
Principles of Machine Learning
This is a first course on machine learning. It aims to provide a foundation for future work with machine learning. This course will get you to the point where you can confidently engage with literature referencing machine learning, but it is not designed to get you to the point where you can actively use modern machine learning methods in your own research. It will however signpost for you which of our other courses will be relevant if you want to get to that stage.
This course also aims to provide the tools to create machine learning models in R using the CARET Library. This is a pre-requisite for the intermediate and advanced courses on supervised and unsupervised learning courses.