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This PhD project is part of a larger project that aims to explain the uncertainty of Machine Learning (ML) predictions. To this effect, we must quantify uncertainty, devise algorithms that explain
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and Boulton, 1968; Wallace and Dowe, 1999a; Wallace, 2005) is a Bayesian information-theoretic principle in machine learning, statistics and data science. MML can be thought of in different ways - it
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and oral communication skills, including drafting of academic papers and grants High level computer skills with software skills such as Microsoft Office, SAS, SPSS If this sounds like you, we highly
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. Required knowledge Python programming Machine learning background Image analysis Video analysis Audio analysis
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of Machine Learning as the problem of approximating function f from the pair of measurements (x,y), and Optimization as the problem of finding the value of input x that maximizes the output y given
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technologies will affect them. It is our anticipation that the work will commence with, in parallel, the survey for collecting the data and a comparison of machine learning methods on artificial pseudo-randomly
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the brain. This wouldn't be a typical machine learning PhD, as many aspects can only be examined on a philosophical and theoretical level. There may be scope to implement aspects in the ideas you develop
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. They generally rely on expert rules or machine learning models to provide health advice. Recently, generative AI tools, such as ChatGPT, have become a popular focus of research. In healthcare, they show strong
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Advisory System, or data from other implantable or wearable devices. This involves consideration of both feature-based machine learning or data science approaches and neural mass parameter estimation
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healthcare, finance, environmental monitoring, and beyond. While recent advancements in foundation models have shown tremendous success in NLP and computer vision, the unique characteristics of time series