Statistics data mining machine par ivezic zeljko (17 résultats)
Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data (Princeton Series in Modern Observational Astronomy (1))
Ivezic, Zeljko, Connolly, Andrew J., VanderPlas, Jacob T, Gray, Alexander
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HRD. Etat : New. New Book. Shipped from UK. Established seller since 2000.
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Etat : New. 2019. Revised edition. Hardcover. . . . . .
Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data, Updated Edition (Princeton Series in Modern Observational Astronomy)
Ivezić, Željko; Connolly, Andrew J.; VanderPlas, Jacob T.; Gray, Alexander
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Etat : New. Revised edition NO-PA16APR2015-KAP.
Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data, Updated Edition (Princeton Series in Modern Observational Astronomy)
Ivezić, Željko; Connolly, Andrew J.; VanderPlas, Jacob T.; Gray, Alexander
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Vendeur : Majestic Books, Hounslow, Royaume-UniMajestic Books
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Vendeur : Brook Bookstore On Demand, Napoli, NA, ItalieBrook Bookstore On Demand
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Etat : new.
Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data, Updated Edition (Princeton Series in Modern Observational Astronomy)
Ivezić, Željko; Connolly, Andrew J.; VanderPlas, Jacob T.; Gray, Alexander
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Vendeur : Ria Christie Collections, Uxbridge, Royaume-UniRia Christie Collections
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Hardcover. Etat : new. Hardcover. Statistics, Data Mining, and Machine Learning in Astronomy is the essential introduction to the statistical methods needed to analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the Large Synoptic Surve…y Telescope. Now fully updated, it presents a wealth of practical analysis problems, evaluates the techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. Python code and sample data sets are provided for all applications described in the book. The supporting data sets have been carefully selected from contemporary astronomical surveys and are easy to download and use. The accompanying Python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, engage with the different methods, and adapt them to their own fields of interest.An accessible textbook for students and an indispensable reference for researchers, this updated edition features new sections on deep learning methods, hierarchical Bayes modeling, and approximate Bayesian computation. The chapters have been revised throughout and the astroML code has been brought completely up to date.Fully revised and expandedDescribes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data setsFeatures real-world data sets from astronomical surveysUses a freely available Python codebase throughoutIdeal for graduate students, advanced undergraduates, and working astronomers Statistics, Data Mining, and Machine Learning in Astronomy is the essential introduction to the statistical methods needed to analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the Large Synoptic Survey Telescope. Now fully updated, it presents a wealth o Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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EUR 100,38
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Etat : New. 2019. Revised edition. Hardcover. . . . . . Books ship from the US and Ireland.
Statistics, Data Mining, and Machine Learning in Astronomy
Alexander Gray, Jacob T. VanderPlas, Zeljko Ivezic, Andrew J. Connolly
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Vendeur : Rarewaves USA, HEBRON, KY, Etats-UnisRarewaves USA
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EUR 111,48
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Hardback. Etat : New. Statistics, Data Mining, and Machine Learning in Astronomy is the essential introduction to the statistical methods needed to analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the Large Synoptic Survey Telescope.… Now fully updated, it presents a wealth of practical analysis problems, evaluates the techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. Python code and sample data sets are provided for all applications described in the book. The supporting data sets have been carefully selected from contemporary astronomical surveys and are easy to download and use. The accompanying Python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, engage with the different methods, and adapt them to their own fields of interest.An accessible textbook for students and an indispensable reference for researchers, this updated edition features new sections on deep learning methods, hierarchical Bayes modeling, and approximate Bayesian computation. The chapters have been revised throughout and the astroML code has been brought completely up to date.Fully revised and expandedDescribes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data setsFeatures real-world data sets from astronomical surveysUses a freely available Python codebase throughoutIdeal for graduate students, advanced undergraduates, and working astronomers.
Statistics, Data Mining, and Machine Learning in Astronomy
Alexander Gray, Jacob T. VanderPlas, Zeljko Ivezic, Andrew J. Connolly
- Couverture rigide
Vendeur : Rarewaves.com USA, London, LONDO, Royaume-UniRarewaves.com USA
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EUR 121,44
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Hardback. Etat : New. Statistics, Data Mining, and Machine Learning in Astronomy is the essential introduction to the statistical methods needed to analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the Large Synoptic Survey Telescope.… Now fully updated, it presents a wealth of practical analysis problems, evaluates the techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. Python code and sample data sets are provided for all applications described in the book. The supporting data sets have been carefully selected from contemporary astronomical surveys and are easy to download and use. The accompanying Python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, engage with the different methods, and adapt them to their own fields of interest.An accessible textbook for students and an indispensable reference for researchers, this updated edition features new sections on deep learning methods, hierarchical Bayes modeling, and approximate Bayesian computation. The chapters have been revised throughout and the astroML code has been brought completely up to date.Fully revised and expandedDescribes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data setsFeatures real-world data sets from astronomical surveysUses a freely available Python codebase throughoutIdeal for graduate students, advanced undergraduates, and working astronomers.
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Vendeur : Russell Books, Victoria, BC, CanadaRussell Books
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EUR 104,77
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Hardcover. Etat : New. Special order direct from the distributor.
Statistics, Data Mining, and Machine Learning in Astronomy
Alexander Gray, Jacob T. VanderPlas, Zeljko Ivezic, Andrew J. Connolly
- Couverture rigide
Vendeur : Rarewaves USA United, HEBRON, KY, Etats-UnisRarewaves USA United
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EUR 114,42
EUR 42,90 expéditionExpédition nationale : Etats-UnisQuantité disponible : 7 disponible(s)
Hardback. Etat : New. Statistics, Data Mining, and Machine Learning in Astronomy is the essential introduction to the statistical methods needed to analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the Large Synoptic Survey Telescope.… Now fully updated, it presents a wealth of practical analysis problems, evaluates the techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. Python code and sample data sets are provided for all applications described in the book. The supporting data sets have been carefully selected from contemporary astronomical surveys and are easy to download and use. The accompanying Python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, engage with the different methods, and adapt them to their own fields of interest.An accessible textbook for students and an indispensable reference for researchers, this updated edition features new sections on deep learning methods, hierarchical Bayes modeling, and approximate Bayesian computation. The chapters have been revised throughout and the astroML code has been brought completely up to date.Fully revised and expandedDescribes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data setsFeatures real-world data sets from astronomical surveysUses a freely available Python codebase throughoutIdeal for graduate students, advanced undergraduates, and working astronomers.
Statistics, Data Mining, and Machine Learning in Astronomy
Alexander Gray, Jacob T. VanderPlas, Zeljko Ivezic, Andrew J. Connolly
- Couverture rigide
Vendeur : Rarewaves.com UK, London, Royaume-UniRarewaves.com UK
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EUR 114,66
EUR 75,82 expéditionExpédition depuis Royaume-Uni vers Etats-UnisQuantité disponible : 7 disponible(s)
Hardback. Etat : New. Statistics, Data Mining, and Machine Learning in Astronomy is the essential introduction to the statistical methods needed to analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the Large Synoptic Survey Telescope.… Now fully updated, it presents a wealth of practical analysis problems, evaluates the techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. Python code and sample data sets are provided for all applications described in the book. The supporting data sets have been carefully selected from contemporary astronomical surveys and are easy to download and use. The accompanying Python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, engage with the different methods, and adapt them to their own fields of interest.An accessible textbook for students and an indispensable reference for researchers, this updated edition features new sections on deep learning methods, hierarchical Bayes modeling, and approximate Bayesian computation. The chapters have been revised throughout and the astroML code has been brought completely up to date.Fully revised and expandedDescribes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data setsFeatures real-world data sets from astronomical surveysUses a freely available Python codebase throughoutIdeal for graduate students, advanced undergraduates, and working astronomers.
- Couverture rigide
Vendeur : AussieBookSeller, Truganina, VIC, AustralieAussieBookSeller
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EUR 166,73
EUR 31,74 expéditionExpédition depuis Australie vers Etats-UnisQuantité disponible : 1 disponible(s)
Hardcover. Etat : new. Hardcover. Statistics, Data Mining, and Machine Learning in Astronomy is the essential introduction to the statistical methods needed to analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the Large Synoptic Surve…y Telescope. Now fully updated, it presents a wealth of practical analysis problems, evaluates the techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. Python code and sample data sets are provided for all applications described in the book. The supporting data sets have been carefully selected from contemporary astronomical surveys and are easy to download and use. The accompanying Python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, engage with the different methods, and adapt them to their own fields of interest.An accessible textbook for students and an indispensable reference for researchers, this updated edition features new sections on deep learning methods, hierarchical Bayes modeling, and approximate Bayesian computation. The chapters have been revised throughout and the astroML code has been brought completely up to date.Fully revised and expandedDescribes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data setsFeatures real-world data sets from astronomical surveysUses a freely available Python codebase throughoutIdeal for graduate students, advanced undergraduates, and working astronomers Statistics, Data Mining, and Machine Learning in Astronomy is the essential introduction to the statistical methods needed to analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the Large Synoptic Survey Telescope. Now fully updated, it presents a wealth o Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data, Updated Edition (Princeton Series in Modern Observational Astronomy)
Ivezić, Željko; Connolly, Andrew J.; VanderPlas, Jacob T.; Gray, Alexander
- Couverture rigide
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Vendeur : Biblios, frankfurt am main, HESSE, AllemagneBiblios
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