Download or read book Statistics Data Mining and Machine Learning in Astronomy written by Željko Ivezić and published by Princeton University Press. This book was released on 2014-01-12 with total page 550 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Machine Learning for Physics and Astronomy written by Viviana Acquaviva and published by Princeton University Press. This book was released on 2023-08-15 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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)
Download or read book Deep Learning For Physics Research written by Martin Erdmann and published by World Scientific. This book was released on 2021-06-25 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: A core principle of physics is knowledge gained from data. Thus, deep learning has instantly entered physics and may become a new paradigm in basic and applied research.This textbook addresses physics students and physicists who want to understand what deep learning actually means, and what is the potential for their own scientific projects. Being familiar with linear algebra and parameter optimization is sufficient to jump-start deep learning. Adopting a pragmatic approach, basic and advanced applications in physics research are described. Also offered are simple hands-on exercises for implementing deep networks for which python code and training data can be downloaded.
Download or read book Machine Learning with Neural Networks written by Bernhard Mehlig and published by Cambridge University Press. This book was released on 2021-10-28 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: This modern and self-contained book offers a clear and accessible introduction to the important topic of machine learning with neural networks. In addition to describing the mathematical principles of the topic, and its historical evolution, strong connections are drawn with underlying methods from statistical physics and current applications within science and engineering. Closely based around a well-established undergraduate course, this pedagogical text provides a solid understanding of the key aspects of modern machine learning with artificial neural networks, for students in physics, mathematics, and engineering. Numerous exercises expand and reinforce key concepts within the book and allow students to hone their programming skills. Frequent references to current research develop a detailed perspective on the state-of-the-art in machine learning research.
Download or read book Advances in Machine Learning and Data Mining for Astronomy written by Michael J. Way and published by CRC Press. This book was released on 2012-03-29 with total page 744 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Understanding Machine Learning written by Shai Shalev-Shwartz and published by Cambridge University Press. This book was released on 2014-05-19 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
Download or read book Deep Learning and Physics written by Akinori Tanaka and published by Springer Nature. This book was released on 2021-03-24 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: What is deep learning for those who study physics? Is it completely different from physics? Or is it similar? In recent years, machine learning, including deep learning, has begun to be used in various physics studies. Why is that? Is knowing physics useful in machine learning? Conversely, is knowing machine learning useful in physics? This book is devoted to answers of these questions. Starting with basic ideas of physics, neural networks are derived naturally. And you can learn the concepts of deep learning through the words of physics. In fact, the foundation of machine learning can be attributed to physical concepts. Hamiltonians that determine physical systems characterize various machine learning structures. Statistical physics given by Hamiltonians defines machine learning by neural networks. Furthermore, solving inverse problems in physics through machine learning and generalization essentially provides progress and even revolutions in physics. For these reasons, in recent years interdisciplinary research in machine learning and physics has been expanding dramatically. This book is written for anyone who wants to learn, understand, and apply the relationship between deep learning/machine learning and physics. All that is needed to read this book are the basic concepts in physics: energy and Hamiltonians. The concepts of statistical mechanics and the bracket notation of quantum mechanics, which are explained in columns, are used to explain deep learning frameworks. We encourage you to explore this new active field of machine learning and physics, with this book as a map of the continent to be explored.
Download or read book Intelligent Astrophysics written by Ivan Zelinka and published by Springer Nature. This book was released on 2021-04-15 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: This present book discusses the application of the methods to astrophysical data from different perspectives. In this book, the reader will encounter interesting chapters that discuss data processing and pulsars, the complexity and information content of our universe, the use of tessellation in astronomy, characterization and classification of astronomical phenomena, identification of extragalactic objects, classification of pulsars and many other interesting chapters. The authors of these chapters are experts in their field and have been carefully selected to create this book so that the authors present to the community a representative publication that shows a unique fusion of artificial intelligence and astrophysics.
Download or read book Machine Learning Meets Quantum Physics written by Kristof T. Schütt and published by Springer Nature. This book was released on 2020-06-03 with total page 473 pages. Available in PDF, EPUB and Kindle. Book excerpt: Designing molecules and materials with desired properties is an important prerequisite for advancing technology in our modern societies. This requires both the ability to calculate accurate microscopic properties, such as energies, forces and electrostatic multipoles of specific configurations, as well as efficient sampling of potential energy surfaces to obtain corresponding macroscopic properties. Tools that can provide this are accurate first-principles calculations rooted in quantum mechanics, and statistical mechanics, respectively. Unfortunately, they come at a high computational cost that prohibits calculations for large systems and long time-scales, thus presenting a severe bottleneck both for searching the vast chemical compound space and the stupendously many dynamical configurations that a molecule can assume. To overcome this challenge, recently there have been increased efforts to accelerate quantum simulations with machine learning (ML). This emerging interdisciplinary community encompasses chemists, material scientists, physicists, mathematicians and computer scientists, joining forces to contribute to the exciting hot topic of progressing machine learning and AI for molecules and materials. The book that has emerged from a series of workshops provides a snapshot of this rapidly developing field. It contains tutorial material explaining the relevant foundations needed in chemistry, physics as well as machine learning to give an easy starting point for interested readers. In addition, a number of research papers defining the current state-of-the-art are included. The book has five parts (Fundamentals, Incorporating Prior Knowledge, Deep Learning of Atomistic Representations, Atomistic Simulations and Discovery and Design), each prefaced by editorial commentary that puts the respective parts into a broader scientific context.
Download or read book Machine Learning for Planetary Science written by Joern Helbert and published by Elsevier. This book was released on 2022-03-22 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning for Planetary Science presents planetary scientists with a way to introduce machine learning into the research workflow as increasingly large nonlinear datasets are acquired from planetary exploration missions. The book explores research that leverages machine learning methods to enhance our scientific understanding of planetary data and serves as a guide for selecting the right methods and tools for solving a variety of everyday problems in planetary science using machine learning. Illustrating ways to employ machine learning in practice with case studies, the book is clearly organized into four parts to provide thorough context and easy navigation. The book covers a range of issues, from data analysis on the ground to data analysis onboard a spacecraft, and from prioritization of novel or interesting observations to enhanced missions planning. This book is therefore a key resource for planetary scientists working in data analysis, missions planning, and scientific observation. - Includes links to a code repository for sharing codes and examples, some of which include executable Jupyter notebook files that can serve as tutorials - Presents methods applicable to everyday problems faced by planetary scientists and sufficient for analyzing large datasets - Serves as a guide for selecting the right method and tools for applying machine learning to particular analysis problems - Utilizes case studies to illustrate how machine learning methods can be employed in practice
Download or read book The Statistical Physics of Data Assimilation and Machine Learning written by Henry D. I. Abarbanel and published by Cambridge University Press. This book was released on 2022-02-17 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: The theory of data assimilation and machine learning is introduced in an accessible manner for undergraduate and graduate students.
Download or read book Machine Learning Techniques for Space Weather written by Enrico Camporeale and published by Elsevier. This book was released on 2018-05-31 with total page 454 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning Techniques for Space Weather provides a thorough and accessible presentation of machine learning techniques that can be employed by space weather professionals. Additionally, it presents an overview of real-world applications in space science to the machine learning community, offering a bridge between the fields. As this volume demonstrates, real advances in space weather can be gained using nontraditional approaches that take into account nonlinear and complex dynamics, including information theory, nonlinear auto-regression models, neural networks and clustering algorithms. Offering practical techniques for translating the huge amount of information hidden in data into useful knowledge that allows for better prediction, this book is a unique and important resource for space physicists, space weather professionals and computer scientists in related fields. - Collects many representative non-traditional approaches to space weather into a single volume - Covers, in an accessible way, the mathematical background that is not often explained in detail for space scientists - Includes free software in the form of simple MATLAB® scripts that allow for replication of results in the book, also familiarizing readers with algorithms
Download or read book Plasma Physics for Astrophysics written by R. M. Kulsrud and published by Princeton University Press. This book was released on 2005 with total page 488 pages. Available in PDF, EPUB and Kindle. Book excerpt: Designed to teach plasma physics and astrophysics 'from the ground up', this textbook proceeds from the simplest examples through a careful derivation of results and encourages the reader to think for themselves.
Download or read book Deep Learning in Science written by Pierre Baldi and published by Cambridge University Press. This book was released on 2021-07 with total page 387 pages. Available in PDF, EPUB and Kindle. Book excerpt: Rigorous treatment of the theory of deep learning from first principles, with applications to beautiful problems in the natural sciences.
Download or read book Knowledge Discovery in Big Data from Astronomy and Earth Observation written by Petr Skoda and published by Elsevier. This book was released on 2020-04-10 with total page 474 pages. Available in PDF, EPUB and Kindle. Book excerpt: Knowledge Discovery in Big Data from Astronomy and Earth Observation: Astrogeoinformatics bridges the gap between astronomy and geoscience in the context of applications, techniques and key principles of big data. Machine learning and parallel computing are increasingly becoming cross-disciplinary as the phenomena of Big Data is becoming common place. This book provides insight into the common workflows and data science tools used for big data in astronomy and geoscience. After establishing similarity in data gathering, pre-processing and handling, the data science aspects are illustrated in the context of both fields. Software, hardware and algorithms of big data are addressed. Finally, the book offers insight into the emerging science which combines data and expertise from both fields in studying the effect of cosmos on the earth and its inhabitants. - Addresses both astronomy and geosciences in parallel, from a big data perspective - Includes introductory information, key principles, applications and the latest techniques - Well-supported by computing and information science-oriented chapters to introduce the necessary knowledge in these fields
Download or read book Cosmoparticle Physics written by Maxim Yu Khlopov and published by World Scientific. This book was released on 1999 with total page 588 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since the 1980s the cross-disciplinary, multidimensional field of links between cosmology and particle physics has been widely recognised by theorists, studying cosmology, particle and nuclear physics, gravity, as well as by astrophysicists, astronomers, space physicists, experimental particle and nuclear physicists, mathematicians and engineers.The relationship between cosmology and particle physics is now one of the important topics of discussion at any scientific meeting both on astrophysics and high energy physics.Cosmoparticle physics is the result of the mutual relationship between cosmology and particle physics in their search for physical mechanisms of inflation, baryosynthesis, nonbaryonic dark matter, and for fundamental unity of the natural forces underlying them. The set of nontrivial links between cosmological consequences of particle models and the astrophysical data on matter and radiation in the modern universe maintains cosmoarcheology, testing self-consistently particular predictions of particle models on the base of cosmological scenarios, following from them. Complex analysis of all the indirect cosmological, astrophysical and microphysical phenomena makes cosmoparticle physics the science of the world and renders quantitatively definite the correspondence between its micro- and macroscopic structure.This book outlines the principal ideas of the modern particle theory and cosmology, their mutual relationship and the nontrivial correspondence of their physical and astrophysical effects.
Download or read book Data Driven Science and Engineering written by Steven L. Brunton and published by Cambridge University Press. This book was released on 2022-05-05 with total page 615 pages. Available in PDF, EPUB and Kindle. Book excerpt: A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.
