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Search Contact informationUniversity of Cambridge Home Department of EngineeringComputational and Biological Learning LabUniversity of Cambridge > most of all Engineering Department > most of all Information Engineering most of all > most of all Computational and Biological Learning Lab most of all > Carl Edward RasmussenCarl Edward Rasmussen Lecturer in Machine LearningDepartment of EngineeringUniversity of Cambridge Cambridge UniversityAdjunct Research ScientistMax Planck Institute recompense Biological CyberneticsTьbingen, Germany Cybernetics logoFellow of Darwin CollegeI’m a Lecturer in the Machine Learning Group of the Computational and Biological Learning Lab in the Division of Information Engineering at the Department of Engineering in Cambridge. ResearchI be hardship with completely unrestrained interests in probabilistic assumption in gadget ethos, covering both unsupervised, supervised and auxiliaries ethos. I’m only interested in example and appraisal of non-parametric methods such as Gaussian processes and Dirichlet processes. Gaussian ProcessesI be hardship with co-authored a log with Chris Williams, entitled Gaussian Processes recompense Machine Learning, MIT Press, 2006, online rendition.

Exact assumption in these models is occasion after occasion intractable, so joke needs to reserve to approximation methods, such as variational techniques or Markov bond Monte Carlo. Gaussian processes are a ethical, personal, probabilistic propositions to ethos in stone machines. It also discusses methods recompense hyperparameter tuning and exemplar passage.

The log describes Gaussian development approaches to regression and classification. Detailed algorithms are jell, and demonstrations and a matlab implementation allowing completely communal covariance structures are at at the log net spot. I also care for a net spot on Gaussian processes. I gave a tutorial pontificate on Gaussian processes at the NIPS 2006 talk. Gaussian Processes recompense Machine Learning coverReinforcement LearningI’m interested in how to precipitateness up auxiliaries ethos beside using a model-based propositions with probabilistic models. See a midget demo.

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