Applied Mathematics for Scientists and Engineers, Raffoul, Youssef
Автор: Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong Название: Mathematics for Machine Learning ISBN: 110845514X ISBN-13(EAN): 9781108455145 Издательство: Cambridge Academ Рейтинг: Цена: 6334.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This self-contained textbook introduces all the relevant mathematical concepts needed to understand and use machine learning methods, with a minimum of prerequisites. Topics include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics.
Автор: Thomas A. Garrity Название: All the Math You Missed ISBN: 1009009192 ISBN-13(EAN): 9781009009195 Издательство: Cambridge Academ Рейтинг: Цена: 3960.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The second edition of this bestselling book provides an overview of the key topics in undergraduate mathematics, allowing beginning graduate students to fill in any gaps in their knowledge. With numerous examples, exercises and suggestions for further reading, it is a must-have for anyone looking to learn some serious mathematics quickly.
Автор: Kadry Название: Mathematics for Engineers and Scientists Labs for Maxima ISBN: 1771887273 ISBN-13(EAN): 9781771887274 Издательство: Taylor&Francis Рейтинг: Цена: 17762.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Mathematics for Engineers and Scientists Labs for Maxima, is designed to be a vital companion to math textbooks covering the topics of pre-calculus, calculus, linear algebra, differential equations, and probability and statistics. The volume will help readers apply nearly all of the Maxima skills discussed here to future courses and research.
Описание: Probability, Statistics, and Information Theory for Scientists and Engineers offers students clear explanations of the most fundamental and useful statistical concepts. Extensively class-tested, the book is designed to appeal to the intuition, yet provide sound academic support, so that conclusions are clear and unambiguous.The text is a graphical introduction to the subject, which allows for ease of interpretation. In the first sections, students will explore the basic axioms and conclusions of probability and statistics. They will learn what can be expected in data taken from certain specified populations, and conversely, what can be inferred about a population from a data set. Unlike other standard texts, Probability, Statistics, and Information Theory for Scientists and Engineers also includes an introduction to information theory—the study of the structure of probability distributions. This allows for a new and useful perspective on hypothesis testing. Designed for science and engineering students, Probability, Statistics and Information Theory for Scientists and Engineers can be used in courses on engineering, math and statistics, and physics.
Автор: Axel Malqvist, Daniel Peterseim Название: Numerical Homogenization by Localized Orthogonal Decomposition ISBN: 1611976448 ISBN-13(EAN): 9781611976441 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 6145.00 р. Наличие на складе: Поставка под заказ.
Описание: This book presents the first survey of the Localized Orthogonal Decomposition (LOD) method, a pioneering approach for the numerical homogenization of partial differential equations with multiscale data beyond periodicity and scale separation. The authors provide a careful error analysis, including previously unpublished results, and a complete implementation of the method in MATLAB. They also reveal how the LOD method relates to classical homogenization and domain decomposition. Illustrated with numerical experiments that demonstrate the significance of the method, the book is enhanced by a survey of applications including eigenvalue problems and evolution problems.Numerical Homogenization by Localized Orthogonal Decomposition is appropriate for graduate students in applied mathematics, numerical analysis, and scientific computing. Researchers in the field of computational partial differential equations will find this self-contained book of interest, as will applied scientists and engineers interested in multiscale simulation.
Автор: Cui, Ying Pang, Jong-shi Название: Modern nonconvex nondifferentiable optimization ISBN: 1611976731 ISBN-13(EAN): 9781611976731 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 14923.00 р. Наличие на складе: Поставка под заказ.
Описание: Starting with the fundamentals of classical smooth optimization and building on established convex programming techniques, this research monograph presents a foundation and methodology for modern nonconvex nondifferentiable optimization. It provides readers with theory, methods, and applications of nonconvex and nondifferentiable optimization in statistical estimation, operations research, machine learning, and decision making. A comprehensive and rigorous treatment of this emergent mathematical topic is urgently needed in today's complex world of big data and machine learning. This book takes a thorough approach to the subject and includes examples and exercises to enrich the main themes, making it suitable for classroom instruction. Modern Nonconvex Nondifferentiable Optimization is intended for applied and computational mathematicians, optimizers, operations researchers, statisticians, computer scientists, engineers, economists, and machine learners. It could be used in advanced courses on optimization/operations research and nonconvex and nonsmooth optimization.
Автор: Richard J. Braun, Tobin A. Driscoll Название: Fundamentals of Numerical Computation: Julia Edition ISBN: 1611977002 ISBN-13(EAN): 9781611977004 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 17879.00 р. Наличие на складе: Поставка под заказ.
Описание: Julia is an open-source and fast-growing programming language for scientific computing that offers clarity and ease of use for beginners but also speed and power for advanced applications. Fundamentals of Numerical Computation: Julia Edition provides a complete solution for teaching Julia in the context of numerical methods.
Автор: John D. Buckmaster Название: The Mathematics of Combustion ISBN: 0898710537 ISBN-13(EAN): 9780898710533 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 11913.00 р. Наличие на складе: Поставка под заказ.
Описание: This book delves into the rapidly changing area of combustion, in which asymptotic methods and bifurcation theory have made a significant impact as have the constant density, small heat release models and other important contributions.
Автор: Charles A. Bouman Название: Foundations of Computational Imaging: A Model-Based Approach ISBN: 1611977126 ISBN-13(EAN): 9781611977127 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 10534.00 р. Наличие на складе: Поставка под заказ.
Описание: Collecting a set of classical and emerging methods that otherwise would not be available in a single treatment, Foundations of Computational Imaging: A Model-Based Approach is the first book to define a common foundation for the mathematical and statistical methods used in computational imaging. The book is designed to bring together an eclectic group of researchers with a wide variety of applications and disciplines including applied math, physics, chemistry, optics, and signal processing, to address a collection of problems that can benefit from a common set of methods. Inside, readers will find:Basic techniques of model-based image processing.A comprehensive treatment of Bayesian and regularized image reconstruction methods.An integrated treatment of advanced reconstruction techniques such as majorization, constrained optimization, ADMM, and Plug-and-Play methods for model integration.Foundations of Computational Imaging can be used in courses on Model-Based or Computational Imaging, Advanced Numerical Analysis, Special Topics on Numerical Analysis, Topics on Data Science, Topics on Numerical Optimization, and Topics on Approximation Theory. It is also for researchers or practitioners in medical imaging, scientific imaging, commercial imaging, or industrial imaging.
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