Physics of Data Science and Machine Learning links fundamental concepts of physics to data science, machine learning, and artificial intelligence for physicists looking to integrate these techniques into their work. This book is written explicitly for physicists, marrying quantum and statistical mechanics with modern data mining, data science, and machine learning. It also explains how to integrate these techniques into the design of experiments, while exploring neural networks and machine learning, building on fundamental concepts of statistical and quantum mechanics. This book is a self-learning tool for physicists looking to learn how to utilize data science and machine learning in their research. It will also be of interest to computer scientists and applied mathematicians, alongside graduate students looking to understand the basic concepts and foundations of data science, machine learning, and artificial intelligence. Although specifically written for physicists, it will also help provide non-physicists with an opportunity to understand the fundamental concepts from a physics perspective to aid in the development of new and innovative machine learning and artificial intelligence tools. Key Features: Introduces the design of experiments and digital twin concepts in simple lay terms for physicists to understand, adopt, and adapt. Free from endless derivations; instead, equations are presented and it is explained strategically why it is imperative to use them and how they will help in the task at hand. Illustrations and simple explanations help readers visualize and absorb the difficult-to-understand concepts. Ijaz A. Rauf is an adjunct professor at the School of Graduate Studies, York University, Toronto, Canada. He is also an associate researcher at Ryerson University, Toronto, Canada and president of the Eminent-Tech Corporation, Bradford, ON, Canada.

"Niels Bohr was a central figure in quantum physics, well-known for his work on atomic structure and his contributions to the Copenhagen interpretation of quantum mechanics. In this book, philosopher Slobodan Perović explores the way Bohr practiced and understood physics, and the implications of this for our understanding of modern science, especially contemporary quantum experimental physics. Perović's method of studying Bohr is philosophical-historical, and his aim is to make sense of both Bohr's understanding of physics and his method of inquiry. He argues that in several important respects, Bohr's vision of physics was driven by his desire to develop a comprehensive perspective on key features of experimental observation as well as emerging experimental work. Perović uncovers how Bohr's distinctive breakthrough contributions are characterized by a multi-layered, phased approach of building on basic experimental insights inductively to develop intermediary and overarching hypotheses. The strengths and limitations of this approach, in contrast to the mathematically or metaphysically driven approaches of other physicists at the time, made him a thoroughly distinctive kind of theorist and scientific leader. Once we see that Bohr played the typical role of a laboratory mediator, and excelled in the inductive process this required, we can fully understand the way his work was generated, the role it played in developing novel quantum concepts, and its true limitations, as well as current adherence to and use of Bohr's complementarity approach among contemporary experimentalists"--

This practical guide covers the essential tasks in statistical data analysis encountered in high energy physics and provides comprehensive advice for typical questions and problems. The basic methods for inferring results from data are presented as well as tools for advanced tasks such as improving the signal-to-background ratio, correcting detector effects, determining systematics and many others. Concrete applications are discussed in analysis walkthroughs. Each chapter is supplemented by numerous examples and exercises and by a list of literature and relevant links. The book targets a broad readership at all career levels - from students to senior researchers. An accompanying website provides more algorithms as well as up-to-date information and links. * Free solutions manual available for lecturers at www.wiley-vch.de/supplements/

A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.

The theory of data assimilation and machine learning is introduced in an accessible manner for undergraduate and graduate students.

A master station list containing information on the location of stations or observatories active in one or more of the disciplines of solar-terrestrial physics was compiled. The alphabetical listing includes the station coordinates, geomagnetic coordinates, conjugate geomagnetic coordinates, L-shell value, invariant latitude value, computed geocentric magnetic dip, and opening and closed dates for each of the observing stations. The vertical cutoff rigidity and station altitude are included for cosmic-ray stations. In addition to the alphabetical listing, a second listing of the master station list by geomagnetic latitude is included. Separate individual discipline listings are also presented.

The revised second edition of this textbook provides the reader with a solid foundation in probability theory and statistics as applied to the physical sciences, engineering and related fields. It covers a broad range of numerical and analytical methods that are essential for the correct analysis of scientific data, including probability theory, distribution functions of statistics, fits to two-dimensional data and parameter estimation, Monte Carlo methods and Markov chains. Features new to this edition include: • a discussion of statistical techniques employed in business science, such as multiple regression analysis of multivariate datasets. • a new chapter on the various measures of the mean including logarithmic averages. • new chapters on systematic errors and intrinsic scatter, and on the fitting of data with bivariate errors. • a new case study and additional worked examples. • mathematical derivations and theoretical background material have been appropriately marked, to improve the readability of the text. • end-of-chapter summary boxes, for easy reference. As in the first edition, the main pedagogical method is a theory-then-application approach, where emphasis is placed first on a sound understanding of the underlying theory of a topic, which becomes the basis for an efficient and practical application of the material. The level is appropriate for undergraduates and beginning graduate students, and as a reference for the experienced researcher. Basic calculus is used in some of the derivations, and no previous background in probability and statistics is required. The book includes many numerical tables of data, as well as exercises and examples to aid the readers' understanding of the topic.