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Synopsis
Trains researchers and graduate students in state-of-the-art statistical and machine learning methods to build models with real-world data.
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About the Authors
Concha Bielza is a professor in the Department of Artificial Intelligence at Universidad Politécnica de Madrid. She has published more than 120 journal papers and coauthored the book Industrial Applications of Machine Learning (2019). She was awarded the 2014 UPM Research Prize.
Pedro Larrañaga is a professor in the Department of Artificial Intelligence at Universidad Politécnica de Madrid. He has published more than 150 journal papers and coauthored the book Industrial Applications of Machine Learning (2019). He is fellow of the European Association for Artificial Intelligence and of Academia Europaea.
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Data-Driven Computational Neuroscience: Machine Learning and Statistical Models
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Cambridge University Press CUP, 2020
ISBN 10: 110849370X
ISBN 13: 9781108493703
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Data-Driven Computational Neuroscience: Machine Learning and Statistical Models
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Data-Driven Computational Neuroscience : Machine Learning and Statistical Models
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Data-Driven Computational Neuroscience: Machine Learning and Statistical Models
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ISBN 13: 9781108493703
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Data-Driven Computational Neuroscience : Machine Learning and Statistical Models
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ISBN 10: 110849370X
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Data-Driven Computational Neuroscience: Machine Learning and Statistical Models
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Data-Driven Computational Neuroscience (Hardcover)
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Cambridge University Press, Cambridge, 2020
ISBN 10: 110849370X
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Hardcover. Condition: new. Hardcover. Data-driven computational neuroscience facilitates the transformation of data into insights into the structure and functions of the brain. This introduction for researchers and graduate students is the first in-depth, comprehensive treatment of statistical and machine learning methods for neuroscience. The methods are demonstrated through case studies of real problems to empower readers to build their own solutions. The book covers a wide variety of methods, including supervised classification with non-probabilistic models (nearest-neighbors, classification trees, rule induction, artificial neural networks and support vector machines) and probabilistic models (discriminant analysis, logistic regression and Bayesian network classifiers), meta-classifiers, multi-dimensional classifiers and feature subset selection methods. Other parts of the book are devoted to association discovery with probabilistic graphical models (Bayesian networks and Markov networks) and spatial statistics with point processes (complete spatial randomness and cluster, regular and Gibbs processes). Cellular, structural, functional, medical and behavioral neuroscience levels are considered. Data-driven computational neuroscience facilitates the transformation of data into insights into the structure and functions of the brain. This modern treatment of real world cases offers neuroscience researchers and graduate students a comprehensive, in-depth guide to statistical and machine learning methods. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781108493703
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Data-Driven Computational Neuroscience: Machine Learning and Statistical Models
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Data-driven computational neuroscience facilitates the transformation of data into insights into the structure and functions of the brain. This modern treatment of real world cases offers neuroscience researchers and graduate students a comprehensive, in-de. Seller Inventory # 371718980
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Data-driven Computational Neuroscience: Machine Learning and Statistical Models
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Data-Driven Computational Neuroscience: Machine Learning and Statistical Models
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