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Real Analysis With Proof Strategies

Typically undergraduates see real analysis as one of the most diﬃcult courses that a mathematics major is required to take. The main reason for this perception is twofold: Students must comprehend new abstract concepts and learn to deal with these concepts on a level of rigor and proof not previously encountered. A key challenge for an instructor of real analysis is to ﬁnd a way to bridge the gap between a student’s preparation and the mathematical skills that are required to be successful in such a course. Real Analysis: With Proof Strategies provides a resolution to the bridging-the-gap problem. The book not only presents the fundamental theorems of real analysis but also shows the reader how to compose and produce the proofs of these theorems. The detail rigor and proof strategies oﬀered in this textbook will be appreciated by all readers. Features Explicitly shows the reader how to produce and compose the proofs of the basic theorems in real analysis Suitable for junior or senior undergraduates majoring in mathematics. | Real Analysis With Proof Strategies

GBP 42.99

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A Modern Introduction to Linear Algebra

Useful Concepts and Results at the Heart of Linear AlgebraA one- or two-semester course for a wide variety of students at the sophomore/junior undergraduate levelA Modern Introduction to Linear Algebra provides a rigorous yet accessible matrix-oriented introduction to the essential concepts of linear algebra. Concrete easy-to-understand examples motivate the theory. The book first discusses vectors Gaussian elimination and reduced row echelon forms. It then offers a thorough introduction to matrix algebra including defining the determinant naturally from the PA=LU factorization of a matrix. The author goes on to cover finite-dimensional real vector spaces infinite-dimensional spaces linear transformations and complex vector spaces. The final chapter presents Hermitian and normal matrices as well as quadratic forms. Taking a computational algebraic and geometric approach to the subject this book provides the foundation for later courses in higher mathematics. It also shows how linear algebra can be used in various areas of application. Although written in a pencil and paper manner the text offers ample opportunities to enhance learning with calculators or computer usage. Solutions manual available for qualifying instructors

GBP 59.99

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Field Guide to Compelling Analytics

Field Guide to Compelling Analytics is written for Analytics Professionals (APs) who want to increase their probability of success in implementing analytical solutions. In the past soft skills such as presentation and persuasive writing techniques have been the extent of teaching junior APs how to effectively communicate the value of analytical products. However there are other aspects to success such as trust and experience that may play a more important role in convincing fellow APs clients advisors and leadership groups that their analytic solutions will work. This book introduces the formula ‘Analytics + Trust + Communication + Experience > Convince Them’ to illustrate an AP’s ability to convince a stakeholder. The ‘Convince Me’ stakeholders might be an analytics team member team lead decision-maker or senior leader that are either internal or external to the AP’s organization. Whoever they are this formula represents a concise digestible and above all practical means to increase the likelihood that you will be able to persuade them of the value of your analytical product. Features Includes insight questions to support class discussion Written in broadly non-mathematical terms designed to be accessible to any level of student or practicing AP to read understand and implement the concepts Each section introduces the ideas through real-life case studies

GBP 48.99

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Transformational Plane Geometry

Designed for a one-semester course at the junior undergraduate level Transformational Plane Geometry takes a hands-on interactive approach to teaching plane geometry. The book is self-contained defining basic concepts from linear and abstract algebra gradually as needed. The text adheres to the National Council of Teachers of Mathematics Principles and Standards for School Mathematics and the Common Core State Standards Initiative Standards for Mathematical Practice. Future teachers will acquire the skills needed to effectively apply these standards in their classrooms. Following Felix Klein’s Erlangen Program the book provides students in pure mathematics and students in teacher training programs with a concrete visual alternative to Euclid’s purely axiomatic approach to plane geometry. It enables geometrical visualization in three ways: Key concepts are motivated with exploratory activities using software specifically designed for performing geometrical constructions such as Geometer’s Sketchpad. Each concept is introduced synthetically (without coordinates) and analytically (with coordinates). Exercises include numerous geometric constructions that use a reflecting instrument such as a MIRA. After reviewing the essential principles of classical Euclidean geometry the book covers general transformations of the plane with particular attention to translations rotations reflections stretches and their compositions. The authors apply these transformations to study congruence similarity and symmetry of plane figures and to classify the isometries and similarities of the plane.

GBP 56.99

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Generative Adversarial Networks and Deep Learning Theory and Applications

This book explores how to use generative adversarial networks in a variety of applications and emphasises their substantial advancements over traditional generative models. This book's major goal is to concentrate on cutting-edge research in deep learning and generative adversarial networks which includes creating new tools and methods for processing text images and audio. A Generative Adversarial Network (GAN) is a class of machine learning framework and is the next emerging network in deep learning applications. Generative Adversarial Networks(GANs) have the feasibility to build improved models as they can generate the sample data as per application requirements. There are various applications of GAN in science and technology including computer vision security multimedia and advertisements image generation image translation text-to-images synthesis video synthesis generating high-resolution images drug discovery etc. Features: Presents a comprehensive guide on how to use GAN for images and videos. Includes case studies of Underwater Image Enhancement Using Generative Adversarial Network Intrusion detection using GAN Highlights the inclusion of gaming effects using deep learning methods Examines the significant technological advancements in GAN and its real-world application. Discusses as GAN challenges and optimal solutions The book addresses scientific aspects for a wider audience such as junior and senior engineering undergraduate and postgraduate students researchers and anyone interested in the trends development and opportunities in GAN and Deep Learning. The material in the book can serve as a reference in libraries accreditation agencies government agencies and especially the academic institution of higher education intending to launch or reform their engineering curriculum | Generative Adversarial Networks and Deep Learning Theory and Applications

GBP 140.00

1

Reinforcement Learning for Cyber-Physical Systems with Cybersecurity Case Studies

Reinforcement Learning for Cyber-Physical Systems: with Cybersecurity Case Studies was inspired by recent developments in the fields of reinforcement learning (RL) and cyber-physical systems (CPSs). Rooted in behavioral psychology RL is one of the primary strands of machine learning. Different from other machine learning algorithms such as supervised learning and unsupervised learning the key feature of RL is its unique learning paradigm i. e. trial-and-error. Combined with the deep neural networks deep RL become so powerful that many complicated systems can be automatically managed by AI agents at a superhuman level. On the other hand CPSs are envisioned to revolutionize our society in the near future. Such examples include the emerging smart buildings intelligent transportation and electric grids. However the conventional hand-programming controller in CPSs could neither handle the increasing complexity of the system nor automatically adapt itself to new situations that it has never encountered before. The problem of how to apply the existing deep RL algorithms or develop new RL algorithms to enable the real-time adaptive CPSs remains open. This book aims to establish a linkage between the two domains by systematically introducing RL foundations and algorithms each supported by one or a few state-of-the-art CPS examples to help readers understand the intuition and usefulness of RL techniques. FeaturesIntroduces reinforcement learning including advanced topics in RL Applies reinforcement learning to cyber-physical systems and cybersecurity Contains state-of-the-art examples and exercises in each chapterProvides two cybersecurity case studies Reinforcement Learning for Cyber-Physical Systems with Cybersecurity Case Studies is an ideal text for graduate students or junior/senior undergraduates in the fields of science engineering computer science or applied mathematics. It would also prove useful to researchers and engineers interested in cybersecurity RL and CPS. The only background knowledge required to appreciate the book is a basic knowledge of calculus and probability theory.

GBP 44.99

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A First Course in Machine Learning

A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility starts from a detailed explanation of the basic foundations of Bayesian analysis in the simplest of settings and goes all the way to the frontiers of the subject such as infinite mixture models GPs and MCMC. —Devdatt Dubhashi Professor Department of Computer Science and Engineering Chalmers University Sweden This textbook manages to be easier to read than other comparable books in the subject while retaining all the rigorous treatment needed. The new chapters put it at the forefront of the field by covering topics that have become mainstream in machine learning over the last decade. —Daniel Barbara George Mason University Fairfax Virginia USA The new edition of A First Course in Machine Learning by Rogers and Girolami is an excellent introduction to the use of statistical methods in machine learning. The book introduces concepts such as mathematical modeling inference and prediction providing ‘just in time’ the essential background on linear algebra calculus and probability theory that the reader needs to understand these concepts. —Daniel Ortiz-Arroyo Associate Professor Aalborg University Esbjerg Denmark I was impressed by how closely the material aligns with the needs of an introductory course on machine learning which is its greatest strength…Overall this is a pragmatic and helpful book which is well-aligned to the needs of an introductory course and one that I will be looking at for my own students in coming months. —David Clifton University of Oxford UK The first edition of this book was already an excellent introductory text on machine learning for an advanced undergraduate or taught masters level course or indeed for anybody who wants to learn about an interesting and important field of computer science. The additional chapters of advanced material on Gaussian process MCMC and mixture modeling provide an ideal basis for practical projects without disturbing the very clear and readable exposition of the basics contained in the first part of the book. —Gavin Cawley Senior Lecturer School of Computing Sciences University of East Anglia UK This book could be used for junior/senior undergraduate students or first-year graduate students as well as individuals who want to explore the field of machine learning…The book introduces not only the concepts but the underlying ideas on algorithm implementation from a critical thinking perspective. —Guangzhi Qu Oakland University Rochester Michigan USA

GBP 39.99

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