INTERSECT provide a lot of training material for RSEs. The training material cover topics such as continuous integration, Git, collaboration, licensing, packaging, performance, reproducibility, software engineering among others.

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Developed by the SSI.

This course aims to teach a core set of established, intermediate-level software development skills and best practices for working as part of a team in a research environment using Python as an example programming language. The core set of skills we teach is not a comprehensive set of all-encompassing skills, but a selective set of tried-and-tested collaborative development skills that forms a firm foundation for continuing on your learning journey.

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AstraZeneca's course version

Developed by the SSI & funded by the SFC.

This lesson explores key topics on the responsible application of machine learning. The lesson is presented as a series of case studies that illustrate real world examples. Sections cover a broad range of topics, including reproducibility, bias, and interpretability. Broadly the topics are ordered chronologically, appearing as they would when thinking through a research study.

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Developed by the SSI & funded by the SFC. 

This lesson gives an introduction to artificial neural networks. We begin by an outlining an important application of machine learning in healthcare: the development of algorithms for classification of chest X-ray images. During the lesson we explore how to prepare and visualise data for algorithm development, and construct a neural net that is able to classify disease.

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Developed by the SSI & funded by the SFC.

Decision trees are a family of algorithms that are based around a tree-like structure of decision rules. These algorithms often perform well in tasks such as prediction and classification. This lesson explores the properties of tree models in the context of mortality prediction.

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Developed by the SSI & funded by the SFC.

This lesson provides an introduction to some of the common methods and terminologies used in machine learning research. We cover areas such as data preparation and resampling, model building, and model evaluation.

It is a prerequisite for the other lessons in the machine learning curriculum. In later lessons we explore tree-based models for prediction, neural networks for image classification, and responsible machine learning.

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