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.
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.
For additional information and to register your interest, follow this link.
Applied Unsupervised Machine Learning
This course on unsupervised learning provides a systematic introduction to dimensionality reduction and clustering techniques. The course covers fundamental concepts of unsupervised learning and data normalization, then progresses through the practical applications of Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and hierarchical clustering algorithms.
The course emphasizes both theoretical understanding and hands-on application, teaching students to recognize when different techniques are appropriate and when they may fail. A key learning objective is understanding the limitations of linear methods like PCA. Students learn to evaluate the performance of unsupervised learning methods across diverse data types, with the ultimate goal of generating meaningful hypotheses for further research.
For additional information and to register your interest, follow this link.