Teaching and Learning

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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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

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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