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This Masters or PhD project aims to explain the uncertainty of Machine Learning (ML) predictions. To this effect, we must quantify uncertainty, devise algorithms that explain ML predictions and
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Many machine learning (ML) approaches have been applied to biomedical data but without substantial applications due to the poor interpretability of models. Although ML approaches have shown
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Background and Motivation Modern deep learning models have achieved remarkable success in computer vision and natural language processing. However, they typically produce overconfident predictions
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Machine learning, dynamical systems theory, control theory, signal processing, network theory, neuroscience are all relevant and a student should have strong knowledge in at least one of these and a
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reinforcement learning. In International conference on machine learning (pp. 2107-2128). PMLR. - Péron, M., Becker, K., Bartlett, P., & Chades, I. (2017, February). Fast-tracking stationary MOMDPs for adaptive
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algorithms for inverse molecular design. High-impact publications in premier machine learning conferences (e.g., ICML, NeurIPS, ICLR) and leading interdisciplinary journals at the intersection of AI and
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methods dealing with model complexity - e.g., AIC, BIC, MDL, MML - can enhance deep learning. References: D. L. Dowe (2008a), "Foreword re C. S. Wallace", Computer Journal, Vol. 51, No. 5 (Sept. 2008
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while inferring underlying physiological changes. Required knowledge Machine learning, dynamical systems theory, control theory, signal processing, time series analysis, neuroscience are all relevant and
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. This work combines computational modelling and simulation with biological experiments that are analysed using cutting-edge computer vision techniques. We collaborate closely with Macquarie University where
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Machine learning is being used to make important decisions affecting people's lives, such as filter loan applicants, deploy police officers, and inform bail and parole decisions, among other things