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of Decision Trees, Support Vector Machines and Logistic Regression Models based on Pre-Computation", IEEE Transactions on Dependable and Secure Computing 2019 #digitalhealth
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testing approaches that can be used to verify that machine learning models are not biased. Required knowledge Software engineering, software testing, statistics, machine learning
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system to unlock important information from unstructured data sources including X-ray images, surgical and radiology text reports. We will compare prediction models based on existing, routinely collected
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I specialise in the numerical modelling of high-energy particle collisions , such as those occurring at the Large Hadron Collider. Accordingly, most projects I offer straddle the intersection
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events with the GOTO telescope network. Projects focussing on thermonuclear bursts will involve analysis of new and archival data from satellite-based X-ray telescopes, and running numerical models
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for helping humans meet this challenge are causal Bayesian networks, which can accurately model complex probabilistic systems. However, because people are notoriously deficient in probabilistic reasoning
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We live and work in a world of complex relationships between data, systems, knowledge, people, documents, biology, software, society, politics, commerce and so on. We can model these relationships
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The world is dynamic and in a constant state of flux, yet most machine learning models learn static models from a dataset that represents a single snapshot in time. My group's research is
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This project aims to harness big data from ubiquitous smartphone sensors to reduce the impact of road transport on the environment. Specifically, we’ll design novel data modelling and indexing
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, modeling uncertainty in data sources and queries, and exploiting rich information from several data sources. Required knowledge Required knowledge Essential First class Honors or Masters degree including