Machine Learning For Physics And Astronomy
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Machine Learning For Physics And Astronomy
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Author : Viviana Acquaviva
language : en
Publisher: Princeton University Press
Release Date : 2023-05-23
Machine Learning For Physics And Astronomy written by Viviana Acquaviva and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-23 with Science categories.
A hands-on introduction to machine learning and its applications to the physical sciences As the size and complexity of data continue to grow exponentially across the physical sciences, machine learning is helping scientists to sift through and analyze this information while driving breathtaking advances in quantum physics, astronomy, cosmology, and beyond. This incisive textbook covers the basics of building, diagnosing, optimizing, and deploying machine learning methods to solve research problems in physics and astronomy, with an emphasis on critical thinking and the scientific method. Using a hands-on approach to learning, Machine Learning for Physics and Astronomy draws on real-world, publicly available data as well as examples taken directly from the frontiers of research, from identifying galaxy morphology from images to identifying the signature of standard model particles in simulations at the Large Hadron Collider. Introduces readers to best practices in data-driven problem-solving, from preliminary data exploration and cleaning to selecting the best method for a given task Each chapter is accompanied by Jupyter Notebook worksheets in Python that enable students to explore key concepts Includes a wealth of review questions and quizzes Ideal for advanced undergraduate and early graduate students in STEM disciplines such as physics, computer science, engineering, and applied mathematics Accessible to self-learners with a basic knowledge of linear algebra and calculus Slides and assessment questions (available only to instructors)
Machine Learning For Astrophysics
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Author : Filomena Bufano
language : en
Publisher: Springer Nature
Release Date : 2023-10-14
Machine Learning For Astrophysics written by Filomena Bufano and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-14 with Science categories.
This book reviews the state of the art in the exploitation of machine learning techniques for the astrophysics community and gives the reader a complete overview of the field. The contributed chapters allow the reader to easily digest the material through balanced theoretical and numerical methods and tools with applications in different fields of theoretical and observational astronomy. The book helps the reader to really understand and quantify both the opportunities and limitations of using machine learning in several fields of astrophysics.
Machine Learning In Heliophysics
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Author : Thomas Berger
language : en
Publisher: Frontiers Media SA
Release Date : 2021-11-24
Machine Learning In Heliophysics written by Thomas Berger and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-24 with Science categories.
Statistics Data Mining And Machine Learning In Astronomy
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Author : Željko Ivezić
language : en
Publisher: Princeton University Press
Release Date : 2014-01-12
Statistics Data Mining And Machine Learning In Astronomy written by Željko Ivezić and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-01-12 with Science categories.
As telescopes, detectors, and computers grow ever more powerful, the volume of data at the disposal of astronomers and astrophysicists will enter the petabyte domain, providing accurate measurements for billions of celestial objects. This book provides a comprehensive and accessible introduction to the cutting-edge statistical methods needed to efficiently analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the upcoming Large Synoptic Survey Telescope. It serves as a practical handbook for graduate students and advanced undergraduates in physics and astronomy, and as an indispensable reference for researchers. Statistics, Data Mining, and Machine Learning in Astronomy presents a wealth of practical analysis problems, evaluates techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. For all applications described in the book, Python code and example data sets are provided. The supporting data sets have been carefully selected from contemporary astronomical surveys (for example, the Sloan Digital Sky Survey) 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, evaluate the methods, and adapt them to their own fields of interest. Describes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data sets Features real-world data sets from contemporary astronomical surveys Uses a freely available Python codebase throughout Ideal for students and working astronomers
Statistics Data Mining And Machine Learning In Astronomy
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Author : Željko Ivezić
language : en
Publisher: Princeton University Press
Release Date : 2020
Statistics Data Mining And Machine Learning In Astronomy written by Željko Ivezić and has been published by Princeton University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Computers categories.
"As telescopes, detectors, and computers grow ever more powerful, the volume of data at the disposal of astronomers and astrophysicists will enter the petabyte domain, providing accurate measurements for billions of celestial objects. This book provides a comprehensive and accessible introduction to the cutting-edge statistical methods needed to efficiently analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the upcoming Large Synoptic Survey Telescope. It serves as a practical handbook for graduate students and advanced undergraduates in physics and astronomy, and as an indispensable reference for researchers. The updates in this new edition will include fixing "code rot," correcting errata, and adding some new sections. In particular, the new sections include new material on deep learning methods, hierarchical Bayes modeling, and approximate Bayesian computation. Statistics, Data Mining, and Machine Learning in Astronomy presents a wealth of practical analysis problems, evaluates techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. For all applications described in the book, Python code and example data sets are provided. The supporting data sets have been carefully selected from contemporary astronomical surveys (for example, the Sloan Digital Sky Survey) 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, evaluate the methods, and adapt them to their own fields of interest"--
Advances In Machine Learning And Data Mining For Astronomy
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Author : Michael J. Way
language : en
Publisher: CRC Press
Release Date : 2012-03-29
Advances In Machine Learning And Data Mining For Astronomy written by Michael J. Way and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-03-29 with Computers categories.
Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines
Applications Of Statistical Methods And Machine Learning In The Space Sciences
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Author : Bala Poduval
language : en
Publisher: Frontiers Media SA
Release Date : 2023-04-12
Applications Of Statistical Methods And Machine Learning In The Space Sciences written by Bala Poduval and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-12 with Science categories.
Machine Learning Under Resource Constraints Discovery In Physics
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Author : Katharina Morik
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2022-12-31
Machine Learning Under Resource Constraints Discovery In Physics written by Katharina Morik and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-31 with Science categories.
Machine Learning under Resource Constraints addresses novel machine learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the computing machinery. The resources are runtime, memory, communication, and energy. Hence, modern computer architectures play a significant role. Novel machine learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are executed on diverse architectures to save resources. It provides a comprehensive overview of the novel approaches to machine learning research that consider resource constraints, as well as the application of the described methods in various domains of science and engineering. Volume 2 covers machine learning for knowledge discovery in particle and astroparticle physics. Their instruments, e.g., particle detectors or telescopes, gather petabytes of data. Here, machine learning is necessary not only to process the vast amounts of data and to detect the relevant examples efficiently, but also as part of the knowledge discovery process itself. The physical knowledge is encoded in simulations that are used to train the machine learning models. At the same time, the interpretation of the learned models serves to expand the physical knowledge. This results in a cycle of theory enhancement supported by machine learning.
Machine Learning
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Author : Dr. Gowthami S
language : en
Publisher: RK Publication
Release Date : 2025-06-17
Machine Learning written by Dr. Gowthami S and has been published by RK Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-17 with Computers categories.
This book offers a comprehensive introduction to Machine Learning, covering fundamental concepts, algorithms, and practical applications. Designed for students, researchers, and professionals, it explores supervised, unsupervised, and reinforcement learning with real-world use cases. Emphasis is placed on model evaluation, optimization, and ethical AI practices in modern data-driven environments.
Systematic Approaches For Integrating Machine Learning With Block Chaining
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Author : Dr. Dhaneshwar Mardi
language : en
Publisher: SK Research Group of Companies
Release Date : 2023-02-27
Systematic Approaches For Integrating Machine Learning With Block Chaining written by Dr. Dhaneshwar Mardi and has been published by SK Research Group of Companies this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-27 with Computers categories.
Dr. Dhaneshwar Mardi, Assistant Professor, Department of Computer Science & Engineering, School of Technology, GITAM University, Visakhapatnam, Andhra Pradesh, India. Dr. Panga Venkata Lakshmi, Professor, Department of Computer Science & Engineering, School of Technology, GITAM University, Visakhapatnam, Andhra Pradesh, India. Dr. Varri Uma Sankararao, Assistant Professor, Department of Computer Science & Engineering, School of Technology, GITAM University, Visakhapatnam, Andhra Pradesh, India. Dr. Sreerama Kanaka Raghu, Assistant Professor, Department of Computer Science and Engineering, School of Technology, GITAM University,Visakhapatnam, Andhra Pradesh, India. Dr. Nitalaksheswara Rao Kolukula, Assistant Professor, Department of Computer Science & Engineering, School of Technology, GITAM University,Visakhapatnam, Andhra Pradesh, India.