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The Mathematics of Machine Learning - A Research Conference of the Cantab Capital Institute for the Mathematics of InformationEpisode from the podcastThe Mathematics of Machine Learning - A Research Conference of the Cantab Capital Institute for the Mathematics of Information
Welcome and Introduction. Update from Cantab Capital Institute for the Mathematics of Information

Welcome and Introduction. Update from Cantab Capital Institute for the Mathematics of Information

Released Tuesday, 29th May 2018
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Welcome and Introduction. Update from Cantab Capital Institute for the Mathematics of Information

Welcome and Introduction. Update from Cantab Capital Institute for the Mathematics of Information

Welcome and Introduction. Update from Cantab Capital Institute for the Mathematics of Information

Welcome and Introduction. Update from Cantab Capital Institute for the Mathematics of Information

Tuesday, 29th May 2018
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Killeya, MSchönlieb, CThursday 24th May 2018 - 10:00 to 10:10

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BackgroundThe Cantab Capital Institute for the Mathematics of Information (CCIMI) was pleased to announce its second annual academic conference, which focused on the academic interactions taking place related to the mathematics of machine learning.Launched in 2016, CCIMI accommodates research activity on fundamental mathematical problems and methodology for understanding, analysing, processing and simulating data. Data science research performed in the Institute is of the highest international level, aiming to extract the relevant information from large and high-dimensional data with a predictable certainty.This event followed previous successful academic and industrial engagement events, which focused on different aspects related to the mathematics of information.Aims and ObjectivesThis one day conference brought together those academics working to advance data science and provided an update on research and collaborations taking place at CCIMI, associated challenges and other potential collaborative opportunities, it also highlighted projects being developed elsewhere related to machine learning.The programme of talks covered some of the following areas:Complexity theorySignal processingPartial differential equationsDeep neural networks.There was a session for short “elevator pitches” from next generation researchers, who also had the opportunity to present more detail about their work in a poster exhibition, which ran during the lunch and the drinks/networking session. This event was of interest to participants including social scientists; physicists; engineers; biomedical scientists as well as those working in statistics; pure, applied and computational analysis; quantum computing, cryptography, communication and security and those from data processing.

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