Statistics for Chemical Engineers: From Data to Models to Decisions (1版)

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【簡介】 Build a firm foundation for studying statistical modelling, data science, and machine learning with this practical introduction to statistics, written with chemical engineers in mind. It introduces a data-model-decision approach to applying statistical methods to real-world chemical engineering challenges, establishes links between statistics, probability, linear algebra, calculus, and optimization, and covers classical and modern topics such as uncertainty quantification, risk modelling, and decision-making under uncertainty. Over 100 worked examples using Matlab and Python demonstrate how to apply theory to practice, with over 70 end-of-chapter problems to reinforce student learning, and key topics are introduced using a modular structure, which supports learning at a range of paces and levels. Requiring only a basic understanding of calculus and linear algebra, this textbook is the ideal introduction for undergraduate students in chemical engineering, and a valuable preparatory text for advanced courses in data science and machine learning with chemical engineering applications.

Introduction to Statistics and Data Analysis for Physicists

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【簡介】 The tools of statistical analysis for experiments in modern physical applications are increasingly sophisticated and specific tools are needed to reliably extract results from complex data. This textbook thus presents a comprehensive treatment of the topic for the practicing physicist, focusing less on mathematical foundations but appealing to intuitive techniques with a large number of examples. This fourth edition is greatly expanded with new sub-topics not covered in standard textbooks. We begin with fundamental probability concepts and measurement errors, continuing to the indispensable Monte Carlo simulation. Likelihood and its underlying likelihood principle are explored, serving as bases for the sections on parameter inference and the treatment of distorted data. Topics like hypothesis testing, the statistics of weighted events, the elimination of nuisance parameters, and deconvolution are updated with new developments. Final chapters introduce other advanced techniques such as statistical learning and bootstrap sampling. Developed and greatly expanded from a graduate course at the University of Siegen, this book serves as an essential resource for all graduate students and researchers seeking a rigorous foundation in statistical methods for experimental physics, especially those in nuclear, particle and astrophysics. 【目錄】

Probing the Past with Data Analytics and AI (1版)

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This comprehensive compendium explains the technical challenges and opportunities behind the most recent and successful applications in artificial intelligence [AI] and data analytics. It focuses on applications that have the power to be adapted to many different fields and explains how AI can be implemented as an assistant in digital humanities. It also introduces new methods and applications in classification trees, networks, and Bayesian learning. The useful reference text benefits professionals, academics, researchers, and graduate students in AI/machine learning, neural networks, and bioinformatics, and digital humanities.

Continuous Optimization for Data Science (1版)

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The text is divided into three main parts: unconstrained optimization, constrained optimization, and linear programming. The first part addresses unconstrained optimization in single-variable and multivariable functions, introducing key algorithms such as steepest descent, Newton, and quasi-Newton methods. The second part focuses on constrained optimization, starting with linear equality constraints and extending to more general cases, including inequality constraints. It details optimality conditions, sensitivity analysis, and relevant algorithms for solving these problems. The third part covers linear programming, presenting the formulation of LP problems, the simplex algorithm, and sensitivity analysis. Throughout, the text provides numerous applications to data science, such as linear regression, maximum likelihood estimation, expectation-maximization algorithms, support vector machines, and linear neural networks.

Data Strategy and AI Value Creation: For Data Leaders by Data Leaders (1版)

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【簡介】 This book is a collection of chapters explaining specific important topic for data leaders across various industries. Written by data leaders for data leaders, each chapter explains a key issue of our time, its impact, its challenges and how it had/could be solved.The book chapters are arranged according to two themes: Theme 1, Data Strategy and Governance, focuses on defining effective data strategy to enable data driven executive decision making and ensuring that data assets are protected and useful.Theme 2, AI Value Creation, discusses how organizations can leverage AI to increase the value of data, solve real problems and create new opportunities.Together, the chapters address contemporary areas of interest and concern through the sharing of experiences, what-to-do, and what-to-watch-out-for.