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27 Jan 2023: communications. computational neuroscience. computer vision and image processing. machine learning. speech recognition, machine translation and dialog systems. ... Current research interests include Bayesian approaches to machine learning, artificial

4M22. Climate Change Mitigation. 4M23. Electricity and Environment (TPE22). 4M24. Computational Statistics and Machine Learning. ... 4G3. Computational Neuroscience. 4G5. Materials and Molecules: Modelling, Simulation and Machine Learning.

27 Jan 2023: He was co-founder of Geometric Intelligence (now Uber AI Labs) and advises a number of AI and machine learning companies. ... His academic career includes concurrent appointments as one of the founding members of the Gatsby Computational Neuroscience

7 Jul 2024: Computational neuroscience (Department of Engineering). This course covers basic topics in computational neuroscience and demonstrates how mathematical analysis and ideas from dynamical systems, machine learning, optimal control and probabilistic

3 Jul 2024: group. David was a terrific researcher and teacher in machine learning, and a passionate campaigner for social good through his work on energy. ... The post-holder will be a member of both CFI, and the Machine Learning Group, run by Prof Zoubin Ghahramani

describe models of plasticity and learning and how they apply to the basic paradigms of machine learning (supervised, unsupervised, reinforcement) as well as pattern formation in the nervous system. ... Content. The course covers basic topics in

describe models of plasticity and learning and how they apply to the basic paradigms of machine learning (supervised, unsupervised, reinforcement) as well as pattern formation in the nervous system. ... Content. The course covers basic topics in

describe models of plasticity and learning and how they apply to the basic paradigms of machine learning (supervised, unsupervised, reinforcement) as well as pattern formation in the nervous system. ... Content. The course covers basic topics in

describe models of plasticity and learning and how they apply to the basic paradigms of machine learning (supervised, unsupervised, reinforcement) as well as pattern formation in the nervous system. ... Content. The course covers basic topics in

describe models of plasticity and learning and how they apply to the basic paradigms of machine learning (supervised, unsupervised, reinforcement) as well as pattern formation in the nervous system. ... Content. The course covers basic topics in