Teaching and Learning

Doing Data Science in the Real World

This course - Advanced Data Science - looks at the real world challenges of data science, separating them into three stages: access, assess and address. The stages help in understanding that the data science pipeline is not just about the machine learning methods, but the ethical concerns, the challenges of data management as well as model fitting.

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Understanding Machine Learning and AI

This course - Machine Learning and Adaptive Intelligence - was originally delivered at the University of Sheffield (2011-2015), but has been updated with current material to introduce key concepts and methods in machine learning.

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Designing machine learning for real-world challenges

This course - Machine Learning and the Physical World - is focused on how to build machine learning systems that interact directly with the real world. It explores how to create models with a principled treatment of uncertainty, allowing researchers to leverage prior knowledge and provide decisions that can be interrogated.

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Python Programming for Science

This self-learning module introduces the fundamentals of Python, some of the kinds of data it can handle, and how to store that data. Designed for researchers across disciplines, it supports learners to rapidly learn how to code in the context of working with real-world data. To access the module, register at the link.

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Introducing data science for science

The Accelerate Programme offers PhDs and postdocs disciplines across Cambridge University the opportunity to participate in a funded 'Data for Science' training course. This structured Accelerate-Spark Data for Science Residency will equips scientists with modern practical data analysis skills using Python in a virtual instructor-led accelerated masterclass.

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Software & Code

netts - NETworks of Transcript Semantics

The algorithms in this toolbox create a semantic speech graph from transcribed speech. Speech transcripts are short paragraphs of largely raw, uncleaned speech-like text.

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Machine learning for science Jupyter notebooks

This gallery brings together Jupyter notebooks produced by participants in the Data Science for Science Residency. They contain information and code about the data science techniques that participants have used in research areas that range from genetics to astronomy.

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Additional Resources

Accelerate's 2021 Annual Symposium

2021's Annual Symposium convened researchers from across Cambridge to explore how AI is advancing their work, and what action is needed to support its wider deployment for scientific discovery. Check out videos and Symposium visuals here.

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Machine Learning Accelerator

2021's Accelerate Science winter school brought together researchers at the interface of machine learning and the sciences to share insights and methods in machine learning that can support scientific discovery.

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