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.
An Introduction to Machine Learning
Machine learning gives computers the ability to learn without being explicitly programmed. It encompasses a broad range of approaches to data analysis with applicability across the biological sciences. Lectures will introduce commonly used algorithms and provide insight into their theoretical underpinnings. In the practicals students will apply these algorithms to real biological data-sets using the R language and environment.
This course is not returning in its current form. It is under review and in the process of being updated. Please watch this space for more information once it becomes available.
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.
For additional information and to register your interest, follow this link.
Programming for Machine Learning
This course is aimed 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.
For additional information and to register your interest, follow this link.
Principles of Artificial Intelligence
This course is aimed to introduce you to the broad ideas currently in use in Artificial Intelligence. It is similar in style and builds upon the ideas in “Principles of Machine Learning”
This one-day course is in the process of being developed. Please watch this space for further updates.
Intermediate Linear & Non-linear Regression
This course will cover the technical implementation of a linear and nonlinear regression from a machine learning perspective.
This one-day course is in the process of being developed. Please watch this space for further updates.
Intermediate Unsupervised Learning
This course will cover the technical implementation of a range of unsupervised learning algorithms
This two-day course is in the process of being developed. Please watch this space for further updates.
Intermediate Supervised Learning
This course will cover the technical implementation of a range of supervised learning algorithms
For additional information and to register your interest, follow this link.
Artificial Neural Networks and Deep Learning
This course will cover the technical implementation of deep learning in a range of contexts.
This two-day course is in the process of being developed. Please watch this space for further updates.