397 machine-learning-"https:"-"https:"-"https:"-"https:"-"UCL" positions at Monash University
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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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systems. The fast growth, practical achievements and the overall success of modern approaches to AI guarantees that machine learning AI approaches will prevail as a generic computing paradigm, and will find
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an electrical and computer systems engineering degree in the Faculty of Engineering. Total scholarship value $20,000 Number offered One at any time See details Farrell Raharjo Clive Weeks Community Leadership
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species' distributions. This project harnesses research in ecological and agent-based modelling, machine learning, and AI to increase the predictive power of models of species’ distribution shifts via “data
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information about behavioural patterns, but scoring this manually is time consuming. For this reason, machine learning solutions have been developed to automate behavioural prediction [5-12]. DeepLabCut [5] is
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research in areas such as machine learning, clinical decision-support, medical imaging, and data-driven health services innovation. The position is expected to develop an independent research profile
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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
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MML for well-behaved models, and has been successfully applied to diverse problems including hypothesis testing, clustering, and machine learning. Aim 1: Theoretical Investigation of MML Properties
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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