Information theory Books
Penguin Books Ltd The Black Swan
Book SynopsisThe phenomenal international bestseller that shows us how to stop trying to predict everything - and take advantage of uncertaintyWhat have the invention of the wheel, Pompeii, the Wall Street Crash, Harry Potter and the internet got in common? Why are all forecasters con-artists? Why should you never run for a train or read a newspaper? This book is all about Black Swans: the random events that underlie our lives, from bestsellers to world disasters. Their impact is huge; they''re impossible to predict; yet after they happen we always try to rationalize them. ''Taleb is a bouncy and even exhilarating guide ... I came to relish what he said, and even develop a sneaking affection for him as a person'' Will Self, Independent on Sunday''He leaps like some superhero of the mind'' Boyd Tonkin, IndependentTrade ReviewA fascinating study of how we are regularly taken for suckers by the unexpected * Guardian *Like the conversation of a raconteur ... hugely enjoyable - compelling * Financial Times *It has altered modern thinking * The Times *Confirms his status as a guru for every would-be Damien Hirst, George Soros and aspirant despot * Sunday Times *The Black Swan changed my view of how the world works -- Daniel Kahneman, author of Thinking, Fast and SlowGreat fun... brash, stubborn, entertaining, opinionated, curious, cajoling -- Stephen J. Dubner, co-author of FreakonomicsThe most prophetic voice of all * GQ *
£12.34
Cambridge University Press How to Prove It
Book SynopsisProofs play a central role in advanced mathematics and theoretical computer science, yet many students struggle the first time they take a course in which proofs play a significant role. This bestselling text''s third edition helps students transition from solving problems to proving theorems by teaching them the techniques needed to read and write proofs. Featuring over 150 new exercises and a new chapter on number theory, this new edition introduces students to the world of advanced mathematics through the mastery of proofs. The book begins with the basic concepts of logic and set theory to familiarize students with the language of mathematics and how it is interpreted. These concepts are used as the basis for an analysis of techniques that can be used to build up complex proofs step by step, using detailed ''scratch work'' sections to expose the machinery of proofs about numbers, sets, relations, and functions. Assuming no background beyond standard high school mathematics, this booTrade Review'Not only does this book help students learn how to prove results, it highlights why we care so much. It starts in the introduction with some simple conjectures and gathering data, quickly disproving the first but amassing support for the second. Will that pattern persist? How can these observations lead us to a proof? The book is engagingly written, and covers - in clear and great detail - many proof techniques. There is a wealth of good exercises at various levels. I've taught problem solving before (at The Ohio State University and Williams College), and this book has been a great addition to the resources I recommend to my students.' Steven J. Miller, Williams College, Massachusetts'This book is my go-to resource for students struggling with how to write mathematical proofs. Beyond its plentiful examples, Velleman clearly lays out the techniques and principles so often glossed over in other texts.' Rafael Frongillo, University of Colorado, Boulder'I've been using this book religiously for the last eight years. It builds a strong foundation in proof writing and creates the axiomatic framework for future higher-level mathematics courses. Even when teaching more advanced courses, I recommend students to read chapter 3 (Proofs) since it is, in my opinion, the best written exposition of proof writing techniques and strategies. This third edition brings a new chapter (Number Theory), which gives the instructor a few more topics to choose from when teaching a fundamental course in mathematics. I will keep using it and recommending it to everyone, professors and students alike.' Mihai Bailesteanu, Central Connecticut State University'Professor Velleman sets himself the difficult task of bridging the gap between algorithmic and proof-based mathematics. By focusing on the basic ideas, he succeeded admirably. Many similar books are available, but none are more treasured by beginning students. In the Third Edition, the constant pursuit of excellence is further reinforced.' Taje Ramsamujh, Florida International University'Proofs are central to mathematical development. They are the tools used by mathematicians to establish and communicate their results. The developing mathematician often learns what constitutes a proof and how to present it by osmosis. How to Prove It aims at changing that. It offers a systematic introduction to the development, structuring, and presentation of logical mathematical arguments, i.e. proofs. The approach is based on the language of first-order logic and supported by proof techniques in the style of natural deduction. The art of proving is exercised with naive set theory and elementary number theory throughout the book. As such, it will prove invaluable to first-year undergraduate students in mathematics and computer science.' Marcelo Fiore, University of Cambridge'Overall, this is an engagingly-written and effective book for illuminating thinking about and building a careful foundation in proof techniques. I could see it working in an introduction to proof course or a course introducing discrete mathematics topics alongside proof techniques. As a self-study guide, I could see it working as it so well engages the reader, depending on how able they are to navigate the cultural context in some examples.' Peter Rowlett, LMS Newsletter'Altogether this is an ambitious and largely very successful introduction to the writing of good proofs, laced with many good examples and exercises, and with a pleasantly informal style to make the material attractive and less daunting than the length of the book might suggest. I particularly liked the many discussions of fallacious or incomplete proofs, and the associated challenges to readers to untangle the errors in proofs and to decide for themselves whether a result is true.' Peter Giblin, University of Liverpool, The Mathematical GazetteTable of Contents1. Sentential logic; 2. Quantificational logic; 3. Proofs; 4. Relations; 5. Functions; 6. Mathematical induction; 7. Number theory; 8. Infinite sets.
£35.99
£19.95
Gingko Press The Medium is the Massage
Book Synopsis
£12.71
Manning Publications Self-Sovereign Identity: Decentralized digital
Book Synopsis"This book is a comprehensive roadmap to the most crucial fix for today's broken Internet." - Brian Behlendorf, GM for Blockchain, Healthcare and Identity at the Linux Foundation In a world of changing privacy regulations, identity theft, and online anonymity, identity is a precious and complex concept. Self-Sovereign Identity (SSI) is a set of technologies that move control of digital identity from third party “identity providers”directly to individuals, and it promises to be one of the most important trendsfor the coming decades. Now in Self-Sovereign Identity, privacy and personal data experts Drummond Reed and Alex Preukschat lay out a roadmap for a futureof personal sovereignty powered by the Blockchain and cryptography. Cutting through the technical jargon with dozens of practical use cases from experts across all major industries, it presents a clear and compelling argument for why SSI is a paradigm shift, and shows how you can be ready to be prepared forit. about the technology Trust onthe internet is at an all-time low. Large corporations and institutions control our personal data because we've never had a simple, safe, strong way to prove who we are online. Self-sovereign identity (SSI) changes all that. about the book In Self-Sovereign Identity: Decentralized digital identity and verifiable credentials, you'll learn how SSI empowers us to receive digitally-signed credentials, store them in private wallets, and securely prove our online identities. It combines a clear, jargon-free introduction to this blockchain-inspired paradigm shift with interesting essays written by its leading practitioners. Whether for property transfer, ebanking, frictionless travel, or personalized services, the SSI model for digital trust will reshape our collective future. what's inside · The architecture of SSI software and services · The technical, legal, and governance concepts behind SSI · How SSI affects global business industry-by-industry · Emerging standards for SSI about the reader For technology and business readers. No prior SSI, cryptography, or blockchain experience required. aboutthe author Drummond Reed is the Chief Trust Officer at Evernym, a technology leader in SSI. Alex Preukschat is the co-founder of SSIMeetup.org and AlianzaBlockchain.org. Trade Review“This book is a comprehensive roadmap to the most crucial fix for today's broken Internet.” Brian Behlendorf, GM for Blockchain, Healthcare and Identity at the Linux Foundation “If trusted relationships over the Internet are important to youor your business, this book is for you.” John Jordan, Executive Director,Trust over IP Foundation “Decentralized identity represents not only a wide range of trust-enabling technologies, but also a paradigm shift in our increasingly digital-first world.” Rouven Heck, Executive Director, Decentralized Identity Foundation
£39.99
Cambridge University Press Mining of Massive Datasets
Written by leading authorities in database and Web technologies, this book is essential reading for students and practitioners alike. The popularity of the Web and Internet commerce provides many extremely large datasets from which information can be gleaned by data mining. This book focuses on practical algorithms that have been used to solve key problems in data mining and can be applied successfully to even the largest datasets. It begins with a discussion of the MapReduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream-processing algorithms for mining data that arrives too fast for exhaustive processing. Other chapters cover the PageRank idea and related tricks for organizing the Web, the problems of finding frequent itemsets, and clustering. This third edition includes new and extended coverage on decision trees, deep learning, and mining social-network graphs.
£64.99
Random House Publishing Group Nexus
Book Synopsis
£22.91
John Wiley & Sons Inc Data Governance For Dummies
Book SynopsisTable of ContentsIntroduction 1 Part 1: Data Everywhere 5 Chapter 1: Defining Data Governance 7 Chapter 2: Exploring a World Awash in Data 23 Chapter 3: Driving Value through Data 41 Chapter 4: Transforming through Data 55 Part 2: Delivering Data Governance 75 Chapter 5: Building the Business Case for Data Governance 77 Chapter 6: Focusing on the Fundamentals of Data Governance 91 Part 3: Developing Data Governance 105 Chapter 7: Establishing Data Governance Objectives 107 Chapter 8: Identifying Data Governance Roles and Responsibilities 121 Chapter 9: Designing a Data Governance Program 139 Chapter 10: Deploying a Data Governance Program 157 Part 4: Democratizing Data 183 Chapter 11: Running a Successful Data Governance Program 185 Chapter 12: Measuring and Monitoring a Data Governance Program 209 Chapter 13: Responding to Data Governance Challenges and Risks. 227 Part 5: The Part of Tens 243 Chapter 14: Ten Data Governance Best Practices 245 Chapter 15: Ten Essential Data Governance Stakeholders 255 Index 263
£19.99
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Understanding Cryptography: A Textbook for
Book SynopsisCryptography is now ubiquitous – moving beyond the traditional environments, such as government communications and banking systems, we see cryptographic techniques realized in Web browsers, e-mail programs, cell phones, manufacturing systems, embedded software, smart buildings, cars, and even medical implants. Today's designers need a comprehensive understanding of applied cryptography. After an introduction to cryptography and data security, the authors explain the main techniques in modern cryptography, with chapters addressing stream ciphers, the Data Encryption Standard (DES) and 3DES, the Advanced Encryption Standard (AES), block ciphers, the RSA cryptosystem, public-key cryptosystems based on the discrete logarithm problem, elliptic-curve cryptography (ECC), digital signatures, hash functions, Message Authentication Codes (MACs), and methods for key establishment, including certificates and public-key infrastructure (PKI). Throughout the book, the authors focus on communicating the essentials and keeping the mathematics to a minimum, and they move quickly from explaining the foundations to describing practical implementations, including recent topics such as lightweight ciphers for RFIDs and mobile devices, and current key-length recommendations. The authors have considerable experience teaching applied cryptography to engineering and computer science students and to professionals, and they make extensive use of examples, problems, and chapter reviews, while the book’s website offers slides, projects and links to further resources. This is a suitable textbook for graduate and advanced undergraduate courses and also for self-study by engineers.The authors' website (http://www.crypto-textbook.com/) provides extensive notes, slides, video lectures; the authors' YouTube channel (https://www.youtube.com/channel/UC1usFRN4LCMcflV7UjHNuQg) includes video lectures.Trade ReviewFrom the reviews: "The authors have succeeded in creating a highly valuable introduction to the subject of applied cryptography. I hope that it can serve as a guide for practitioners to build more secure systems based on cryptography, and as a stepping stone for future researchers to explore the exciting world of cryptography and its applications." (Bart Preneel, K.U.Leuven) "The material is very well presented so it is clear to understand. The necessary amount of mathematics is used and complete yet simple examples are used by the authors to help the reader understand the topics. ... [The authors] appear to fully understand the concepts and follow a very good pedagogical process that helps the reader not only understand the different topics but motivate you to perform some of the exercises at the end of each chapter and browse some of the reference materials. I fully recommend this book to any software developer/designer working or considering working on a project that requires security." (John Canessa) "The book presents a panoramic of modern Cryptography with a view to practical applications. ... The book is well written, many examples and figures through it illustrate the theory and the book's website offers links and supplementary information. The book also discusses the implementation in software and hardware of the main algorithms described." (Juan Tena Ayuso, Zentralblatt MATH, Vol. 1190, 2010)Table of ContentsIntroduction to Cryptography and Data Security.- Stream Ciphers.- The Data Encryption Standard (DES) and Alternatives.- The Advanced Encryption Standard (AES).- More About Block Ciphers.- to Public-Key Cryptography.- The RSA Cryptosystem.- Public-Key Cryptosystems Based on the Discrete Logarithm Problem.- Elliptic Curve Cryptosystems.- Digital Signatures.- Hash Functions.- Message Authentication Codes (MACs).- Key Establishment.
£29.69
Emerald Publishing Limited Mathematical and Economic Theory of Road Pricing
Book SynopsisProvides the methodological advances in applying advanced modeling techniques to road pricing. This book discusses topics such as: fundamentals of traffic equilibrium problems; principle of marginal-cost road pricing; models and algorithms for the general second-best road pricing problems; social and spatial equities; Pareto pricing; and more.Trade ReviewRoad pricing is an idea whose time has come. As we invoke new information and computer technologies to monitor, manage and operate or transportation systems more efficiently and effectively, and as we wrestle with significant and growing problems of environment, congestion, energy and equity affecting urban transport, new and alternative solutions are needed. We have to make urban transportation more sustainable, more responsive, and more intelligent. Road pricing is one approach to achieving these goals, a smarter way of running a transportation system. Whilst the concepts and constructs of road pricing have been well known for some time, the methods and technology to employ them have only recently arrived, and the socio-political will to implement road pricing is just beginning to appear. Professor Yang is one of the world's leading transportation scientists and has devoted his recent research to the topic of road pricing, leading to the development of theories and models for analysing and optimising the likely impacts of road pricing schemes, and to methods for selecting the appropriate scheme for a given urban area. This book provides a coherent, in-depth and intelligible treatment of the topic, drawing on Professor Yangs extensive research, knowledge and understanding. Its account of the topic, its history, capabilities and complexities, and alternative approaches and potential for application to cities around the world will be of interest to transportation engineers and planners, both in research and practice. It is set to become a seminal work on road pricing and will be of value to academics, researchers, policy analysts and students in transportation engineering and planning. Professor M.A.P. Taylor, University of South AustraliaTable of ContentsIntroduction. Fundamentals of User-Equilibrium Problems. The First-Best Road Pricing Problems. The Second-Best Road Pricing Problems: A Sensitivity Analysis Based Approach. The Second-Best Road Pricing Problems: A Gap Function Based Approach. Discriminatory and Anonymous Road Pricing. Social and Spatial Equities and Revenue Redistribution. Pricing, Capacity Choice and Financing. Simultaneous Determination of Optimal Toll Levels and Locations. Sequential Pricing Experiments with Limited Information. Bounding the Efficiency Gain or Loss of Road Pricing. Dynamic Road Pricing: Single and Parallel Bottleneck Models. Dynamic Road Pricing: General Network Models.
£129.19
Oxford University Press Information Ecology
Book SynopsisAccording to virtually every business writer, we are in the midst of a new information age, one that will revolutionize how workers work, how companies compete, perhaps even how thinkers think. And it is certainly true that Information Technology has become a giant industry. In America, more that 50% of all capital spending goes into IT, accounting for more than a third of the growth of the entire American economy in the last four years. Over the last decade, IT spending in the U.S. is estimated at 3 trillion dollars. And yet, by almost all accounts, IT hasn''t worked all that well. Why is it that so many of the companies that rave invested in these costly new technologies never saw the returns they had hoped for? And why do workers, even CEOs, find it so hard to adjust to new IT systems? In Information Ecology, Thomas Davenport proposes a revolutionary new way to look at information management, one that takes into account the total information environment within an organization. ArguiTrade Review"An important, must-read book about managers and their information needs."--F.Warren McFarlan, Albert H. Gordon Professor of Business Administration, Harvard University"Information Ecology defines mobilization for the future, a topic that is clearly thought provoking and one that we must all address if true information technology return on investment is to occur."--Ralph J. Szygenda, Vice President and Chief Executive Officer, General Motors Corporation'...a timely corrective to the technophile culture that has dominated the field of information to date...an informative book for those who want to manage information and not just IT to the best effect within both business and healthcare.' * Chris Atkinson, British Jnl of Healthcare Computing & Information Management, vol.15, Number 5 *A well argued , and well presented, case that needs to be read by all those wrestling with this critical subject. - Stuart MacDonald - Long Range Planning Vol 31 Oct 1988
£57.00
Clarendon Press Every Thing Must Go
Book SynopsisEvery Thing Must Go argues that the only kind of metaphysics that can contribute to objective knowledge is one based specifically on contemporary science as it really is, and not on philosophers'' a priori intuitions, common sense, or simplifications of science. In addition to showing how recent metaphysics has drifted away from connection with all other serious scholarly inquiry as a result of not heeding this restriction, they demonstrate how to build a metaphysics compatible with current fundamental physics (''ontic structural realism''), which, when combined with their metaphysics of the special sciences (''rainforest realism''), can be used to unify physics with the other sciences without reducing these sciences to physics itself. Taking science metaphysically seriously, Ladyman and Ross argue, means that metaphysicians must abandon the picture of the world as composed of self-subsistent individual objects, and the paradigm of causation as the collision of such objects.Everything Trade ReviewThis material is dense, challenging and creative...a provovative book...the authors are to be commended for taking on the challenge to develop a systematic, scientifically informed metaphysics for the twenty-first century. * Paul W. Humphreys Metascience *This challenging and provocative book contends that contemporary fundamental physics carries radically counterintuitive consequences for metaphysics * Jarrett Leplin, Philosophical Papers *an enticing work * Jeremy Butterfield, Times Literary Supplement *Table of ContentsPreface ; 1. In Defence of Scientism ; 2. Scientific Realism, Constructive Empiricism and Structuralism ; 3. Ontic Structural Realism and the Philosophy of Physics ; 4. Rainforest Realism and the Unity of Science ; 5. Causation in a Structural World ; 6. Conclusion - Philosophy Enough ; Bibliography
£119.00
The University of Chicago Press The Economics of Attention Style and Substance
Book SynopsisIf economics is about the allocation of resources, then what is the most precious resource in our information economy? Certainly not information, for we are drowning in it. No, what we are short of is the attention to make sense of that information. This title traces our move from an economy of things and objects to an economy of attention.Trade Review"I personally find this head-smackingly insightful. Of course! Money may make the world go 'round, but it's attention that we increasingly sell, hoard, compete for, and fuss over....The real news is that just about all of us - whether we participate in the market as producers or consumers - live increasingly in the attention economy as well." - Andrew Cassel, Philadelphia Inquirer "Lanham's points are strong and well-researched....If style is going to increasingly operate as the decision-making arbiter, Lanham should be commended on his: clear, jargon-free, and forward-thinking." - Publishers Weekly "It's refreshing to read a deeply literary mind who embraces the information age, and wants to focus on its civilizing possibilities rather than flee from the screens in horror." - Pat Kane, Independent (UK)"
£18.00
The University of Chicago Press Distant Horizons
Book SynopsisTrade Review"Distant Horizons not only proves that Ted Underwood is defining the field of cultural analytics as it emerges; it shows us why. Combining literary theory with a deep understanding of computational methods, this volume demonstrates and effectively argues that quantitative analysis is best used not to find objective truths but to explore perspectives, both historically local and theoretical. It is at once a primer for quantitative literacy and a historically sensitive exploration of gender, genre, character, and audience, putting paid once and for all to the notion that statistical methods have no place in hermeneutics."--Laura Mandell, author of Breaking the Book: Print Humanities in the Digital Age "Distant Horizons is of compelling interest to digital humanists. But its true audience is a wider society of literary and other humanities scholars spanning across fields, periods, approaches, and levels. For this larger audience, Ted Underwood goes out of his way to make distant reading accessible, inviting, and persuasive. This innovative book is the breakout work digital humanists have been waiting for, and it is positioned to be a landmark work in literary scholarship at large."--Alan Liu, author of Friending the Past: The Sense of History in the Digital Age
£22.80
University of Chicago Press Planning as Persuasive Storytelling The
Book SynopsisThis study looks at the world of political conflict surrounding the Commonwealth Edison Company's nuclear power plant construction programme in northern Illinois during the 1980s. It examines the theory that planning can best be thought of as a form of persuasive storytelling.Table of ContentsList of Illustrations List of Tables Preface Acknowledgments Prelude: A Strange Place, an Alien Language 1: The Irony of Modernist Planning 2: The Argumentative or Rhetorical Turn in Planning 3: The Modernist Institution and Rhetoric of Regulated Natural Monopoly 4: Commonwealth Edison's Ambitious Nuclear Power Expansion Plan, 1973- 1986 5: The Best Deal for Illinois Consumers? Assessing Commonwealth Edison's "Negotiated Settlement" 6: Edison Completes Its Nuclear Power Expansion Plan, But Who Will Pay for the Last of It? 7: Precinct Captains at the Nuclear Switch? Exploring Chicago's Electric Power Options 8: Survey Research as a Trope in Electric Power Planning Arguments 9: Precinct Captains at the Nuclear Switch? The Mayor's Hand Turns up Empty 10: Frozen in a Passionate Embrace: Allocating Pain, Allocating Blame 11: The Plateau in the Web: Planning as Persuasive Storytelling within a Web of Relationships Postlude: E-mail to a Friend Notes References Illustration Credits Index
£104.00
The University of Chicago Press Planning as Persuasive Storytelling
Book SynopsisThis study looks at the world of political conflict surrounding the Commonwealth Edison Company's nuclear power plant construction programme in northern Illinois during the 1980s. It examines the theory that planning can best be thought of as a form of persuasive storytelling.
£28.50
University of Texas Press Intercultural Communication
Book SynopsisAn authoritative, practical guide for deciphering and following the rules that govern cultures, with a demonstration of how these rules apply to the communication issues that exist between the United States and Mexico.Table of Contents Preface Acknowledgments Part I. The Global Perspective of Intercultural Communication 1. Why Communicate across Cultures? 2. What Constitutes a Culture? 3. Obstacles of Perception 4. Obstacles in Verbal Processes 5. Obstacles in Nonverbal Processes Part II. Two Worlds: The United States and Mexico 6. The Mexico-United States Cultural Environment 7. Some Mexico-United States Cultural Issues 8. Day-to-Day Cultural Interaction Part III. Conclusion 9. Transcending Culture Appendix: Author's Note Glossary Notes Bibliography Index About the Author
£17.99
Bloomsbury Publishing (UK) Multimodal Discourse The Modes and Media of Contemporary Communication Hodder Arnold Publication
Book SynopsisGunther Kress, IOE, UCL's Faculty of Education and Society, University College London, UKTheo Van Leeuwen, Faculty of Humanities and Social Sciences, University of Technology Sydney, AustraliaTrade Review'Multimodal Discourse is the theoretical browser we need to navigate the exuberant multimodality of the highly mediated owrld in which we live. For students and specialists in communication or semiotics, cultural studies or linguistics, graphic design or anthropology this is the foundationla theory of how meaning is made in this period of increasing semiotic fragmentation and cross-over.' Ron Scollon, Professor of Linguistics, Georgetown
£34.99
Bloomsbury Publishing (UK) Understanding Media Theory Hodder Arnold Publication
Book SynopsisKevin Williams is Professor of Media and Communication Studies at Swansea University. His publications include Get Me a Murder a Day! A History of Mass Communication in Britain (1998) and Shadows and Substance: The Development of a Media Policy for Wales (1997).Trade ReviewExcellent broad range covering the key areas of debate M Macher, Cambridge Regional CollegeTable of ContentsWhat is media theory?; production; content; reception; media policy and research.
£39.89
Springer-Verlag New York Inc. Introduction to Cryptography
Book Synopsis1 Integers.- 2 Congruences and Residue Class Rings.- 3 Encryption.- 4 Probability and Perfect Secrecy.- 5 DES.- 6 AES.- 7 Prime Number Generation.- 8 Public-Key Encryption.- 9 Factoring.- 10 Discrete Logarithms.- 11 Cryptographic Hash Functions.- 12 Digital Signatures.- 13 Other Systems.- 14 Identification.- 15 Secret Sharing.- 16 Public-Key Infrastructures.- Solutions of the exercises.- References.Trade ReviewFrom the reviews: Zentralblatt Math "[......] Of the three books under review, Buchmann's is by far the most sophisticated, complete and up-to-date. It was written for computer-science majors - German ones at that - and might be rough going for all but the best American undergraduates. It is amazing how much Buchmann is able to do in under 300 pages: self-contained explanations of the relevant mathematics (with proofs); a systematic introduction to symmetric cryptosystems, including a detailed description and discussion of DES; a good treatment of primality testing, integer factorization, and algorithms for discrete logarithms, clearly written sections describing most of the major types of cryptosystems, and explanations of basic concepts of practical cryptography such as hash functions, message authentication codes, signatures, passwords, certification authorities, and certificate chains. This book is an excellent reference, and I believe that it would also be a good textbook for a course for mathematics or computer science majors, provided that the instructor is prepared to supplement it with more leisurely treatments of some of the topics." N. Koblitz (Seattle, WA) - American Math. Society Monthly. J.A. Buchmann Introduction to Cryptography "It gives a clear and systematic introduction into the subject whose popularity is ever increasing, and can be recommended to all who would like to learn about cryptography. The book contains many exercises and examples. It can be used as a textbook and is likely to become popular among students. The necessary definitions and concepts from algebra, number theory and probability theory are formulated, illustrated by examples and applied to cryptography." —ZENTRALBLATT MATH "For those of use who wish to learn more about cryptography and/or to teach it, Johannes Buchmann has written this book. … The book is mathematically complete and a satisfying read. There are plenty of homework exercises … . This is a good book for upperclassmen, graduate students, and faculty. … This book makes a superior reference and a fine textbook." (Robert W. Vallin, MathDL, January, 2001) "Buchmann’s book is a text on cryptography intended to be used at the undergraduate level. … the intended audiences of this book are ‘readers who want to learn about modern cryptographic algorithms and their mathematical foundations … . I enjoy reading this book. … Readers will find a good exposition of the techniques used in developing and analyzing these algorithms. … These make Buchmann’s text an excellent choice for self study or as a text for students … in elementary number theory and algebra." (Andrew C. Lee, SIGACT News, Vol. 34 (4), 2003) From the reviews of the second edition: "This is the english translation of the second edition of the author’s prominent german textbook ‘Einführung in die Kryptographie’. The original text grew out of several courses on cryptography given by the author at the Technical University Darmstadt; it is aimed at readers who want to learn about modern cryptographic techniques and its mathematical foundations … . As compared with the first edition the number of exercises has almost been doubled and some material … has been added." (R. Steinbauer, Monatshefte für Mathematik, Vol. 150 (4), 2007)Table of ContentsIntegers.- Congruences and Residue Class Rings.- Encryption.- Probability and Perfect Secrecy.- DES.- AES.- Prime Number Generation.- Public-Key Encryption.- Factoring.- Discrete Logarithms.- Cryptographic Hash Functions.- Digital Signatures.- Other Systems.- Identification.- Public-Key Infrastructures.- Solutions of the Odd Exercises.- Subject Index.- Bibliography.
£53.99
Taylor & Francis Ltd Systems Practice in the Information Society
Book SynopsisAs a collection of ideas and methodologies, systems thinking has made an impact in organizations and in particular in the information systems field. However, this main emphasis on organizations limits the scope of systems thinking and practice. There is a need first to use systems thinking in addressing societal problems, and second to enable people involved in developing the information society to reflect on the impacts of systems and technologies in society as a whole. Thus, there are opportunities to review the scope and potential of systems thinking and practice to deal with information society-related issues. Systems Practice in the Information Society provides students of information systems as well as practicing Inofrmation Systems managers with concepts and strategies to enable them to understand and use systems thinking methodologies and address challenges posed by the development of information-based societies. This book brings experiences, ideaTable of Contents1. Introduction 2. The Information Society 3. Systems-Thinking 4. Applied Systems-Thinking 5. Idealist Pattern for Practice in the Information Society 6. Strategic Pattern of Pracitice for the Information Society 7. Power-based Pattern for Practice in the Information Society 8. A Dynamic Practice Framework for Living and Working in the Information Society 9. Conclusions
£171.00
Elsevier Science Theoretical Foundations of Quantum Computing
Book Synopsis
£65.69
INGRAM PUBLISHER SERVICES US The Master Algorithm
Book Synopsis Recommended by Bill Gates A thought-provoking and wide-ranging exploration of machine learning and the race to build computer intelligences as flexible as our own In the world's top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner--the Master Algorithm--and discusses what it will mean for business, science, and society. If data-ism is today's philosophy, this book is its bible.
£11.99
John Wiley & Sons Inc Distributed Source Coding
Book SynopsisDistributed source coding is one of the key enablers for efficient cooperative communication. The potential applications range from wireless sensor networks, ad-hoc networks, and surveillance networks, to robust low-complexity video coding, stereo/Multiview video coding, HDTV, hyper-spectral and multispectral imaging, and biometrics. The book is divided into three sections: theory, algorithms, and applications. Part one covers the background of information theory with an emphasis on DSC; part two discusses designs of algorithmic solutions for DSC problems, covering the three most important DSC problems: Slepian-Wolf, Wyner-Ziv, and MT source coding; and part three is dedicated to a variety of potential DSC applications. Key features: Clear explanation of distributed source coding theory and algorithms including both lossless and lossy designs. Rich applications of distributed source coding, which covers multimedia communication and data security aTable of ContentsPreface xiii Acknowledgment xv About the Companion Website xvii 1 Introduction 1 1.1 What is Distributed Source Coding? 2 1.2 Historical Overview and Background 2 1.3 Potential and Applications 3 1.4 Outline 4 Part I Theory of Distributed Source Coding 7 2 Lossless Compression of Correlated Sources 9 2.1 Slepian–Wolf Coding 10 2.1.1 Proof of the SWTheorem 15 Achievability of the SWTheorem 16 Converse of the SWTheorem 19 2.2 Asymmetric and Symmetric SWCoding 21 2.3 SWCoding of Multiple Sources 22 3 Wyner–Ziv Coding Theory 25 3.1 Forward Proof ofWZ Coding 27 3.2 Converse Proof of WZ Coding 29 3.3 Examples 30 3.3.1 Doubly Symmetric Binary Source 30 Problem Setup 30 A Proposed Scheme 31 Verify the Optimality of the Proposed Scheme 32 3.3.2 Quadratic Gaussian Source 35 Problem Setup 35 Proposed Scheme 36 Verify the Optimality of the Proposed Scheme 37 3.4 Rate Loss of theWZ Problem 38 Binary Source Case 39 Rate loss of General Cases 39 4 Lossy Distributed Source Coding 41 4.1 Berger–Tung Inner Bound 42 4.1.1 Berger–Tung Scheme 42 Codebook Preparation 42 Encoding 42 Decoding 43 4.1.2 Distortion Analysis 43 4.2 Indirect Multiterminal Source Coding 45 4.2.1 Quadratic Gaussian CEO Problem with Two Encoders 45 Forward Proof of Quadratic Gaussian CEO Problem with Two Terminals 46 Converse Proof of Quadratic Gaussian CEO Problem with Two Terminals 48 4.3 Direct Multiterminal Source Coding 54 4.3.1 Forward Proof of Gaussian Multiterminal Source Coding Problem with Two Sources 55 4.3.2 Converse Proof of Gaussian Multiterminal Source Coding Problem with Two Sources 63 Bounds for R1 and R2 64 Collaborative Lower Bound 66 𝜇-sum Bound 67 Part II Implementation 75 5 Slepian–Wolf Code Designs Based on Channel Coding 77 5.1 Asymmetric SWCoding 77 5.1.1 Binning Idea 78 5.1.2 Syndrome-based Approach 79 Hamming Binning 80 SWEncoding 80 SWDecoding 80 LDPC-based SWCoding 81 5.1.3 Parity-based Approach 82 5.1.4 Syndrome-based Versus Parity-based Approach 84 5.2 Non-asymmetric SWCoding 85 5.2.1 Generalized Syndrome-based Approach 86 5.2.2 Implementation using IRA Codes 88 5.3 Adaptive Slepian–Wolf Coding 90 5.3.1 Particle-based Belief Propagation for SWCoding 91 5.4 Latest Developments and Trends 93 6 Distributed Arithmetic Coding 97 6.1 Arithmetic Coding 97 6.2 Distributed Arithmetic Coding 101 6.3 Definition of the DAC Spectrum 103 6.3.1 Motivations 103 6.3.2 Initial DAC Spectrum 104 6.3.3 Depth-i DAC Spectrum 105 6.3.4 Some Simple Properties of the DAC Spectrum 107 6.4 Formulation of the Initial DAC Spectrum 107 6.5 Explicit Form of the Initial DAC Spectrum 110 6.6 Evolution of the DAC Spectrum 113 6.7 Numerical Calculation of the DAC Spectrum 116 6.7.1 Numerical Calculation of the Initial DAC Spectrum 117 6.7.2 Numerical Estimation of DAC Spectrum Evolution 118 6.8 Analyses on DAC Codes with Spectrum 120 6.8.1 Definition of DAC Codes 121 6.8.2 Codebook Cardinality 122 6.8.3 Codebook Index Distribution 123 6.8.4 Rate Loss 123 6.8.5 Decoder Complexity 124 6.8.6 Decoding Error Probability 126 6.9 Improved Binary DAC Codec 130 6.9.1 Permutated BDAC Codec 130 Principle 130 Proof of SWLimit Achievability 131 6.9.2 BDAC Decoder withWeighted Branching 132 6.10 Implementation of the Improved BDAC Codec 134 6.10.1 Encoder 134 Principle 134 Implementation 135 6.10.2 Decoder 135 Principle 135 Implementation 136 6.11 Experimental Results 138 Effect of Segment Size on Permutation Technique 139 Effect of Surviving-Path Number onWB Technique 139 Comparison with LDPC Codes 139 Application of PBDAC to Nonuniform Sources 140 6.12 Conclusion 141 7 Wyner–Ziv Code Design 143 7.1 Vector Quantization 143 7.2 Lattice Theory 146 7.2.1 What is a Lattice? 146 Examples 146 Dual Lattice 147 Integral Lattice 147 Lattice Quantization 148 7.2.2 What is a Good Lattice? 149 Packing Efficiency 149 Covering Efficiency 150 Normalized Second Moment 150 Kissing Number 150 Some Good Lattices 151 7.3 Nested Lattice Quantization 151 Encoding/decoding 152 Coset Binning 152 Quantization Loss and Binning Loss 153 SW Coded NLQ 154 7.3.1 Trellis Coded Quantization 154 7.3.2 Principle of TCQ 155 Generation of Codebooks 156 Generation of Trellis from Convolutional Codes 156 Mapping of Trellis Branches onto Sub-codebooks 157 Quantization 157 Example 158 7.4 WZ Coding Based on TCQ and LDPC Codes 159 7.4.1 Statistics of TCQ Indices 159 7.4.2 LLR of Trellis Bits 162 7.4.3 LLR of Codeword Bits 163 7.4.4 Minimum MSE Estimation 163 7.4.5 Rate Allocation of Bit-planes 164 7.4.6 Experimental Results 166 Part III Applications 167 8 Wyner–Ziv Video Coding 169 8.1 Basic Principle 169 8.2 Benefits of WZ Video Coding 170 8.3 Key Components of WZ Video Decoding 171 8.3.1 Side-information Preparation 171 Bidirectional Motion Compensation 172 8.3.2 Correlation Modeling 173 Exploiting Spatial Redundancy 174 8.3.3 Rate Controller 175 8.4 Other Notable Features of Miscellaneous WZ Video Coders 175 9 Correlation Estimation in DVC 177 9.1 Background to Correlation Parameter Estimation in DVC 177 9.1.1 Correlation Model inWZ Video Coding 177 9.1.2 Offline Correlation Estimation 178 Pixel Domain Offline Correlation Estimation 178 Transform Domain Offline Correlation Estimation 180 9.1.3 Online Correlation Estimation 181 Pixel Domain Online Correlation Estimation 182 Transform Domain Online Correlation Estimation 184 9.2 Recap of Belief Propagation and Particle Filter Algorithms 185 9.2.1 Belief Propagation Algorithm 185 9.2.2 Particle Filtering 186 9.3 Correlation Estimation in DVC with Particle Filtering 187 9.3.1 Factor Graph Construction 187 9.3.2 Correlation Estimation in DVC with Particle Filtering 190 9.3.3 Experimental Results 192 9.3.4 Conclusion 197 9.4 Low Complexity Correlation Estimation using Expectation Propagation 199 9.4.1 System Architecture 199 9.4.2 Factor Graph Construction 199 Joint Bit-plane SWCoding (Region II) 200 Correlation Parameter Tracking (Region I) 201 9.4.3 Message Passing on the Constructed Factor Graph 202 Expectation Propagation 203 9.4.4 Posterior Approximation of the Correlation Parameter using Expectation Propagation 204 Moment Matching 205 9.4.5 Experimental Results 206 9.4.6 Conclusion 211 10 DSC for Solar Image Compression 213 10.1 Background 213 10.2 RelatedWork 215 10.3 Distributed Multi-view Image Coding 217 10.4 Adaptive Joint Bit-plane WZ Decoding of Multi-view Images with Disparity Estimation 217 10.4.1 Joint Bit-planeWZ Decoding 217 10.4.2 Joint Bit-planeWZ Decoding with Disparity Estimation 219 10.4.3 Joint Bit-planeWZ Decoding with Correlation Estimation 220 10.5 Results and Discussion 221 10.6 Summary 224 11 Secure Distributed Image Coding 225 11.1 Background 225 11.2 System Architecture 227 11.2.1 Compression of Encrypted Data 228 11.2.2 Joint Decompression and Decryption Design 230 11.3 Practical Implementation Issues 233 11.4 Experimental Results 233 11.4.1 Experiment Setup 234 11.4.2 Security and Privacy Protection 235 11.4.3 Compression Performance 236 11.5 Discussion 239 12 Secure Biometric Authentication Using DSC 241 12.1 Background 241 12.2 RelatedWork 243 12.3 System Architecture 245 12.3.1 Feature Extraction 246 12.3.2 Feature Pre-encryption 248 12.3.3 SeDSC Encrypter/decrypter 248 12.3.4 Privacy-preserving Authentication 249 12.4 SeDSC Encrypter Design 249 12.4.1 Non-asymmetric SWCodes with Code Partitioning 250 12.4.2 Implementation of SeDSC Encrypter using IRA Codes 251 12.5 SeDSC Decrypter Design 252 12.6 Experiments 256 12.6.1 Dataset and Experimental Setup 256 12.6.2 Feature Length Selection 257 12.6.3 Authentication Accuracy 257 Authentication Performances on Small Feature Length (i.e., N = 100) 257 Performances on Large Feature Lengths (i.e., N ≥ 300) 258 12.6.4 Privacy and Security 259 12.6.5 Complexity Analysis 261 12.7 Discussion 261 A Basic Information Theory 263 A.1 Information Measures 263 A.1.1 Entropy 263 A.1.2 Relative Entropy 267 A.1.3 Mutual Information 268 A.1.4 Entropy Rate 269 A.2 Independence and Mutual Information 270 A.3 Venn Diagram Interpretation 273 A.4 Convexity and Jensen’s Inequality 274 A.5 Differential Entropy 277 A.5.1 Gaussian Random Variables 278 A.5.2 Entropy Power Inequality 278 A.6 Typicality 279 A.6.1 Jointly Typical Sequences 282 A.7 Packing Lemmas and Covering Lemmas 284 A.8 Shannon’s Source CodingTheorem 286 A.9 Lossy Source Coding—Rate-distortionTheorem 289 A.9.1 Rate-distortion Problem with Side Information 291 B Background on Channel Coding 293 B.1 Linear Block Codes 294 B.1.1 Syndrome Decoding of Block Codes 295 B.1.2 Hamming Codes, Packing Bound, and Perfect Codes 295 B.2 Convolutional Codes 297 B.2.1 Viterbi Decoding Algorithm 298 B.3 Shannon’s Channel CodingTheorem 301 B.3.1 Achievability Proof of the Channel CodingTheorem 303 B.3.2 Converse Proof of Channel CodingTheorem 305 B.4 Low-density Parity-check Codes 306 B.4.1 A Quick Summary of LDPC Codes 306 B.4.2 Belief Propagation Algorithm 307 B.4.3 LDPC Decoding using BP 312 B.4.4 IRA Codes 314 C Approximate Inference 319 C.1 Stochastic Approximation 319 C.1.1 Importance SamplingMethods 320 C.1.2 Markov Chain Monte Carlo 321 Markov Chains 321 Markov Chain Monte Carlo 321 C.2 Deterministic Approximation 322 C.2.1 Preliminaries 322 Exponential Family 322 Kullback–Leibler Divergence 323 Assumed-density Filtering 324 C.2.2 Expectation Propagation 325 Relationship with BP 326 C.2.3 Relationship with Other Variational Inference Methods 328 D Multivariate Gaussian Distribution 331 D.1 Introduction 331 D.2 Probability Density Function 331 D.3 Marginalization 332 D.4 Conditioning 333 D.5 Product of Gaussian pdfs 334 D.6 Division of Gaussian pdfs 337 D.7 Mixture of Gaussians 337 D.7.1 Reduce the Number of Components in Gaussian Mixtures 338 Which Components to Merge? 340 How to Merge Components? 341 D.8 Summary 342 Appendix: Matrix Equations 343 Bibliography 345 Index 357
£118.34
John Wiley & Sons Inc Elements of Information Theory Wiley Series in
Book SynopsisThe latest edition of this classic is updated with new problem sets and material The Second Edition of this fundamental textbook maintains the book''s tradition of clear, thought-provoking instruction. Readers are provided once again with an instructive mix of mathematics, physics, statistics, and information theory. All the essential topics in information theory are covered in detail, including entropy, data compression, channel capacity, rate distortion, network information theory, and hypothesis testing. The authors provide readers with a solid understanding of the underlying theory and applications. Problem sets and a telegraphic summary at the end of each chapter further assist readers. The historical notes that follow each chapter recap the main points. The Second Edition features: * Chapters reorganized to improve teaching * 200 new problems * New material on source coding, portfolio theory, and feedback capacity * Updated referencTrade Review"As expected, the quality of exposition continues to be a high point of the book. Clear explanations, nice graphical illustrations, and illuminating mathematical derivations make the book particularly useful as a textbook on information theory." (Journal of the American Statistical Association, March 2008) "This book is recommended reading, both as a textbook and as a reference." (Computing Reviews.com, December 28, 2006)Table of ContentsContents v Preface to the Second Edition xv Preface to the First Edition xvii Acknowledgments for the Second Edition xxi Acknowledgments for the First Edition xxiii 1 Introduction and Preview 1 1.1 Preview of the Book 5 2 Entropy, Relative Entropy, and Mutual Information 13 2.1 Entropy 13 2.2 Joint Entropy and Conditional Entropy 16 2.3 Relative Entropy and Mutual Information 19 2.4 Relationship Between Entropy and Mutual Information 20 2.5 Chain Rules for Entropy, Relative Entropy, and Mutual Information 22 2.6 Jensen’s Inequality and Its Consequences 25 2.7 Log Sum Inequality and Its Applications 30 2.8 Data-Processing Inequality 34 2.9 Sufficient Statistics 35 2.10 Fano’s Inequality 37 Summary 41 Problems 43 Historical Notes 54 3 Asymptotic Equipartition Property 57 3.1 Asymptotic Equipartition Property Theorem 58 3.2 Consequences of the AEP: Data Compression 60 3.3 High-Probability Sets and the Typical Set 62 Summary 64 Problems 64 Historical Notes 69 4 Entropy Rates of a Stochastic Process 71 4.1 Markov Chains 71 4.2 Entropy Rate 74 4.3 Example: Entropy Rate of a Random Walk on a Weighted Graph 78 4.4 Second Law of Thermodynamics 81 4.5 Functions of Markov Chains 84 Summary 87 Problems 88 Historical Notes 100 5 Data Compression 103 5.1 Examples of Codes 103 5.2 Kraft Inequality 107 5.3 Optimal Codes 110 5.4 Bounds on the Optimal Code Length 112 5.5 Kraft Inequality for Uniquely Decodable Codes 115 5.6 Huffman Codes 118 5.7 Some Comments on Huffman Codes 120 5.8 Optimality of Huffman Codes 123 5.9 Shannon–Fano–Elias Coding 127 5.10 Competitive Optimality of the Shannon Code 130 5.11 Generation of Discrete Distributions from Fair Coins 134 Summary 141 Problems 142 Historical Notes 157 6 Gambling and Data Compression 159 6.1 The Horse Race 159 6.2 Gambling and Side Information 164 6.3 Dependent Horse Races and Entropy Rate 166 6.4 The Entropy of English 168 6.5 Data Compression and Gambling 171 6.6 Gambling Estimate of the Entropy of English 173 Summary 175 Problems 176 Historical Notes 182 7 Channel Capacity 183 7.1 Examples of Channel Capacity 184 7.1.1 Noiseless Binary Channel 184 7.1.2 Noisy Channel with Nonoverlapping Outputs 185 7.1.3 Noisy Typewriter 186 7.1.4 Binary Symmetric Channel 187 7.1.5 Binary Erasure Channel 188 7.2 Symmetric Channels 189 7.3 Properties of Channel Capacity 191 7.4 Preview of the Channel Coding Theorem 191 7.5 Definitions 192 7.6 Jointly Typical Sequences 195 7.7 Channel Coding Theorem 199 7.8 Zero-Error Codes 205 7.9 Fano’s Inequality and the Converse to the Coding Theorem 206 7.10 Equality in the Converse to the Channel Coding Theorem 208 7.11 Hamming Codes 210 7.12 Feedback Capacity 216 7.13 Source–Channel Separation Theorem 218 Summary 222 Problems 223 Historical Notes 240 8 Differential Entropy 243 8.1 Definitions 243 8.2 AEP for Continuous Random Variables 245 8.3 Relation of Differential Entropy to Discrete Entropy 247 8.4 Joint and Conditional Differential Entropy 249 8.5 Relative Entropy and Mutual Information 250 8.6 Properties of Differential Entropy, Relative Entropy, and Mutual Information 252 Summary 256 Problems 256 Historical Notes 259 9 Gaussian Channel 261 9.1 Gaussian Channel: Definitions 263 9.2 Converse to the Coding Theorem for Gaussian Channels 268 9.3 Bandlimited Channels 270 9.4 Parallel Gaussian Channels 274 9.5 Channels with Colored Gaussian Noise 277 9.6 Gaussian Channels with Feedback 280 Summary 289 Problems 290 Historical Notes 299 10 Rate Distortion Theory 301 10.1 Quantization 301 10.2 Definitions 303 10.3 Calculation of the Rate Distortion Function 307 10.3.1 Binary Source 307 10.3.2 Gaussian Source 310 10.3.3 Simultaneous Description of Independent Gaussian Random Variables 312 10.4 Converse to the Rate Distortion Theorem 315 10.5 Achievability of the Rate Distortion Function 318 10.6 Strongly Typical Sequences and Rate Distortion 325 10.7 Characterization of the Rate Distortion Function 329 10.8 Computation of Channel Capacity and the Rate Distortion Function 332 Summary 335 Problems 336 Historical Notes 345 11 Information Theory and Statistics 347 11.1 Method of Types 347 11.2 Law of Large Numbers 355 11.3 Universal Source Coding 357 11.4 Large Deviation Theory 360 11.5 Examples of Sanov’s Theorem 364 11.6 Conditional Limit Theorem 366 11.7 Hypothesis Testing 375 11.8 Chernoff–Stein Lemma 380 11.9 Chernoff Information 384 11.10 Fisher Information and the Cramér–Rao Inequality 392 Summary 397 Problems 399 Historical Notes 408 12 Maximum Entropy 409 12.1 Maximum Entropy Distributions 409 12.2 Examples 411 12.3 Anomalous Maximum Entropy Problem 413 12.4 Spectrum Estimation 415 12.5 Entropy Rates of a Gaussian Process 416 12.6 Burg’s Maximum Entropy Theorem 417 Summary 420 Problems 421 Historical Notes 425 13 Universal Source Coding 427 13.1 Universal Codes and Channel Capacity 428 13.2 Universal Coding for Binary Sequences 433 13.3 Arithmetic Coding 436 13.4 Lempel–Ziv Coding 440 13.4.1 Sliding Window Lempel–Ziv Algorithm 441 13.4.2 Tree-Structured Lempel–Ziv Algorithms 442 13.5 Optimality of Lempel–Ziv Algorithms 443 13.5.1 Sliding Window Lempel–Ziv Algorithms 443 13.5.2 Optimality of Tree-Structured Lempel–Ziv Compression 448 Summary 456 Problems 457 Historical Notes 461 14 Kolmogorov Complexity 463 14.1 Models of Computation 464 14.2 Kolmogorov Complexity: Definitions and Examples 466 14.3 Kolmogorov Complexity and Entropy 473 14.4 Kolmogorov Complexity of Integers 475 14.5 Algorithmically Random and Incompressible Sequences 476 14.6 Universal Probability 480 14.7 Kolmogorov complexity 482 14.8 Ω 484 14.9 Universal Gambling 487 14.10 Occam’s Razor 488 14.11 Kolmogorov Complexity and Universal Probability 490 14.12 Kolmogorov Sufficient Statistic 496 14.13 Minimum Description Length Principle 500 Summary 501 Problems 503 Historical Notes 507 15 Network Information Theory 509 15.1 Gaussian Multiple-User Channels 513 15.1.1 Single-User Gaussian Channel 513 15.1.2 Gaussian Multiple-Access Channel with m Users 514 15.1.3 Gaussian Broadcast Channel 515 15.1.4 Gaussian Relay Channel 516 15.1.5 Gaussian Interference Channel 518 15.1.6 Gaussian Two-Way Channel 519 15.2 Jointly Typical Sequences 520 15.3 Multiple-Access Channel 524 15.3.1 Achievability of the Capacity Region for the Multiple-Access Channel 530 15.3.2 Comments on the Capacity Region for the Multiple-Access Channel 532 15.3.3 Convexity of the Capacity Region of the Multiple-Access Channel 534 15.3.4 Converse for the Multiple-Access Channel 538 15.3.5 m-User Multiple-Access Channels 543 15.3.6 Gaussian Multiple-Access Channels 544 15.4 Encoding of Correlated Sources 549 15.4.1 Achievability of the Slepian–Wolf Theorem 551 15.4.2 Converse for the Slepian–Wolf Theorem 555 15.4.3 Slepian–Wolf Theorem for Many Sources 556 15.4.4 Interpretation of Slepian–Wolf Coding 557 15.5 Duality Between Slepian–Wolf Encoding and Multiple-Access Channels 558 15.6 Broadcast Channel 560 15.6.1 Definitions for a Broadcast Channel 563 15.6.2 Degraded Broadcast Channels 564 15.6.3 Capacity Region for the Degraded Broadcast Channel 565 15.7 Relay Channel 571 15.8 Source Coding with Side Information 575 15.9 Rate Distortion with Side Information 580 15.10 General Multiterminal Networks 587 Summary 594 Problems 596 Historical Notes 609 16 Information Theory and Portfolio Theory 613 16.1 The Stock Market: Some Definitions 613 16.2 Kuhn–Tucker Characterization of the Log-Optimal Portfolio 617 16.3 Asymptotic Optimality of the Log-Optimal Portfolio 619 16.4 Side Information and the Growth Rate 621 16.5 Investment in Stationary Markets 623 16.6 Competitive Optimality of the Log-Optimal Portfolio 627 16.7 Universal Portfolios 629 16.7.1 Finite-Horizon Universal Portfolios 631 16.7.2 Horizon-Free Universal Portfolios 638 16.8 Shannon–McMillan–Breiman Theorem (General AEP) 644 Summary 650 Problems 652 Historical Notes 655 17 Inequalities in Information Theory 657 17.1 Basic Inequalities of Information Theory 657 17.2 Differential Entropy 660 17.3 Bounds on Entropy and Relative Entropy 663 17.4 Inequalities for Types 665 17.5 Combinatorial Bounds on Entropy 666 17.6 Entropy Rates of Subsets 667 17.7 Entropy and Fisher Information 671 17.8 Entropy Power Inequality and Brunn–Minkowski Inequality 674 17.9 Inequalities for Determinants 679 17.10 Inequalities for Ratios of Determinants 683 Summary 686 Problems 686 Historical Notes 687 Bibliography 689 List of Symbols 723 Index 727
£92.66
Cambridge University Press Fundamentals of ErrorCorrecting Codes
Book SynopsisFundamentals of Error Correcting Codes is an in-depth introduction to coding theory from both an engineering and mathematical viewpoint. As well as covering classical topics, there is much coverage of techniques which could only be found in specialist journals and book publications. Numerous exercises and examples and an accessible writing style make this a lucid and effective introduction to coding theory for advanced undergraduate and graduate students, researchers and engineers, whether approaching the subject from a mathematical, engineering or computer science background.Trade ReviewReview of the hardback: 'Numerous exercises and examples and an accessible writing style make this a lucid and effective introduction to coding theory for advanced undergraduate and graduate students, researchers and engineers - whether approaching the subject from a mathematical, engineering or computer science background.' IASI Polytechnic MagazineTable of ContentsPreface; 1. Basic concepts of linear codes; 2. Bounds on size of codes; 3. Finite fields; 4. Cyclic codes; 5. BCH and Reed-Soloman codes; 6. Duadic codes; 7. Weight distributions; 8. Designs; 9. Self-dual codes; 10. Some favourite self-dual codes; 11. Covering radius and cosets; 12. Codes over Z4; 13. Codes from algebraic geometry; 14. Convolutional codes; 15. Soft decision and iterative decoding; Bibliography; Index.
£62.99
Cambridge University Press Lattice Coding for Signals and Networks
Book SynopsisUnifying information theory and digital communication through the language of lattice codes, this book provides a detailed overview for students, researchers and industry practitioners. It covers classical work by leading researchers in the field of lattice codes and complementary work on dithered quantization and infinite constellations, and then introduces the more recent results on ''algebraic binning'' for side-information problems, and linear/lattice codes for networks. It shows how high dimensional lattice codes can close the gap to the optimal information theoretic solution, including the characterisation of error exponents. The solutions presented are based on lattice codes, and are therefore close to practical implementations, with many advanced setups and techniques, such as shaping, entropy-coding, side-information and multi-terminal systems. Moreover, some of the network setups shown demonstrate how lattice codes are potentially more efficient than traditional random-codingTable of Contents1. Introduction; 2. Lattices; 3. Figures of merit; 4. Dithering and estimation; 5. Entropy-coded quantization; 6. Infinite constellation for modulation; 7. Asymptotic goodness; 8. Nested lattices; 9. Lattice shaping; 10. Side-information problems; 11. Modulo-lattice modulation with Yuval Kochman; 12. Gaussian networks with Bobak Nazer; 13. Error exponents.
£158.86
Princeton University Press The Evolution of Biological Information
Book Synopsis
£49.60
Princeton University Press The Physical Nature of Information
Book Synopsis
£44.00
Pluto Press Imaginary Futures From Thinking Machines to the
Book SynopsisThis book is a history of the future. It shows how our contemporary understanding of the Net is shaped by visions of the future that were put together in the 1950s and 1960s.Trade Review'Barbrook has an amusing take on our distorted - if not delusional - relationship with technology, but his underlying point is serious: future visions of technology are used to distract us and also control us, and if we forget these imaginary futures, we are likely to repeat them' -- Guardian Unlimited'A compelling, authoritative, and painstakingly documented narrative, Imaginary Futures traces the emergence of the computer era in the context of desperately competing ideologies, economics, and empires. This is a work of passionate and persuasive scholarship by a contemporary social theorist at the top of his game' -- Douglas Rushkoff, author, Coercion, Media Virus, Get Back in the Box.Table of Contents1. The Future Is What It Used To Be 2. The American Century 3. Cold War Computing 4. The Human Machine 5. Cybernetic Supremacy 6. The Global Village 7. The Cold War Left 8. The Chosen Few 9. Free Workers In The Affluent Society 10. The Prophets Of Post-Industrialism 11. The American Road to Cybernetic Communism 12. The Leader Of The Free World 13. The Great Game 14. The American Invasion Of Vietnam 15. Those Who Forget The Future Are Condemned To Repeat It References Index
£26.99
Pluto Press Imaginary Futures From Thinking Machines to the
Book SynopsisThis book is a history of the future. It shows how our contemporary understanding of the Net is shaped by visions of the future that were put together in the 1950s and 1960s.Trade Review'Barbrook has an amusing take on our distorted - if not delusional - relationship with technology, but his underlying point is serious: future visions of technology are used to distract us and also control us, and if we forget these imaginary futures, we are likely to repeat them' -- Guardian Unlimited'A compelling, authoritative, and painstakingly documented narrative, Imaginary Futures traces the emergence of the computer era in the context of desperately competing ideologies, economics, and empires. This is a work of passionate and persuasive scholarship by a contemporary social theorist at the top of his game' -- Douglas Rushkoff, author, Coercion, Media Virus, Get Back in the Box.Table of Contents1. The Future Is What It Used To Be 2. The American Century 3. Cold War Computing 4. The Human Machine 5. Cybernetic Supremacy 6. The Global Village 7. The Cold War Left 8. The Chosen Few 9. Free Workers In The Affluent Society 10. The Prophets Of Post-Industrialism 11. The American Road to Cybernetic Communism 12. The Leader Of The Free World 13. The Great Game 14. The American Invasion Of Vietnam 15. Those Who Forget The Future Are Condemned To Repeat It References Index
£68.00
Pluto Press Furious Technological Feminism and Digital
Book SynopsisA major work of feminist critical theory challenging the masculinist politics of digital media forms, practices and study.Trade Review'Furious rips beyond the vanity of know-it-all analysis to offer long-awaited new ways of thinking, feeling, and writing. Cunningly crafted by an authorial trio, it bewitches with performative feminist energies. I dare you to read it' -- Sally-Jane Norman, Founding Director of the Attenborough Centre for the Creative Arts at the University of Sussex'A rare gem of a book, Furious makes a sharp, critical feminist intervention in digital media research demonstrating the power of thinking together, going against the conventions of academic writing, and creating good trouble' -- Susanna Paasonen, co-author of 'NSFW: Sex, Humor, and Risk in Social Media''This wide-ranging and imaginative book makes a compelling case for a feminist techno-politics which challenges to the core the masculinist grip of computational culture and science. It's also a book which pays fine attention to the process of writing' -- Angela McRobbie, author of 'Be Creative: Making a Living in the New Culture Industries''A passionate guidebook to feminist theorising that refuses data as self-evident patterns and theory as beautiful abstractions, while insisting on the generative power of writing, fabulation, and future making' -- Lucy Suchman, author of 'Feminist STS and the Sciences of the Artificial''Combining clarity with rational ire, Furious insists on the power of insurgent, intersectional feminist epistemologies to disrupt, inspire, and transform. This collective feminist tour de force writes us a new course, away from the technophilic belief that technology will fix what ails the contemporary world and toward a critical and temperate utopianism' -- Carol Stabile, author of 'The Broadcast 41: Women and the Anti-Communist Blacklist'Table of ContentsSeries Preface Acknowledgements Preface 1. Feminist Futures: A Conditional Paeon for the Anything-Digital 2. Scale, Subject and Stories: Unreal Objects 3. Bland Ambition? Automation's Missing Visions 4. Driving at the Anthropocene, or, Let's Get Out of Here: How? 5. Technological Feminism and Digital Futures Bibliography Index
£68.00
Pluto Press Furious
Book SynopsisA major work of feminist critical theory challenging the masculinist politics of digital media forms, practices and study.Trade Review'Furious rips beyond the vanity of know-it-all analysis to offer long-awaited new ways of thinking, feeling, and writing. Cunningly crafted by an authorial trio, it bewitches with performative feminist energies. I dare you to read it' -- Sally-Jane Norman, Founding Director of the Attenborough Centre for the Creative Arts at the University of Sussex'A rare gem of a book, Furious makes a sharp, critical feminist intervention in digital media research demonstrating the power of thinking together, going against the conventions of academic writing, and creating good trouble' -- Susanna Paasonen, co-author of 'NSFW: Sex, Humor, and Risk in Social Media''This wide-ranging and imaginative book makes a compelling case for a feminist techno-politics which challenges to the core the masculinist grip of computational culture and science. It's also a book which pays fine attention to the process of writing' -- Angela McRobbie, author of 'Be Creative: Making a Living in the New Culture Industries''A passionate guidebook to feminist theorising that refuses data as self-evident patterns and theory as beautiful abstractions, while insisting on the generative power of writing, fabulation, and future making' -- Lucy Suchman, author of 'Feminist STS and the Sciences of the Artificial''Combining clarity with rational ire, Furious insists on the power of insurgent, intersectional feminist epistemologies to disrupt, inspire, and transform. This collective feminist tour de force writes us a new course, away from the technophilic belief that technology will fix what ails the contemporary world and toward a critical and temperate utopianism' -- Carol Stabile, author of 'The Broadcast 41: Women and the Anti-Communist Blacklist'Table of ContentsSeries Preface Acknowledgements Preface 1. Feminist Futures: A Conditional Paeon for the Anything-Digital 2. Scale, Subject and Stories: Unreal Objects 3. Bland Ambition? Automation's Missing Visions 4. Driving at the Anthropocene, or, Let's Get Out of Here: How? 5. Technological Feminism and Digital Futures Bibliography Index
£20.69
University of Minnesota Press Computing as Writing
Book SynopsisWhat does it mean to be a writer today? Is writing code for an app equivalent to writing a novel? Should we change how we teach writing? Computing as Writing ponders both the implications and contradictions of the common metaphor that equates computing and writing, from "notebook" computers to "writing" code.Trade Review"Daniel Punday traces the idea—an idea that he shows to be pervasive—that to control computers we typically engage in a sort of writing. This insight informs our understanding of computation in culture and also enriches our notion of writing generally. It should, additionally, help non-programmer humanists see that, since they have learned to write, they can learn to do that specific type of writing that is known as programming."—Nick Montfort, Massachusetts Institute of Technology"In a world in which the distinction between writing and computing is increasingly blurred, Punday's volume raises some intriguing questions and offers some new ways to look at writing and computing."—CHOICETable of ContentsContentsPreface1. My Documents: Remembering the Memex2. Writing, Work, and Profession3. Programmer as Writer4. E-books, Libraries, and Feelies5. Invention, Patents, and the Technological System6. Audience Today: Between Literature and PerformanceConclusion: Invention, Creativity, and the Teaching of WritingAcknowledgmentsNotesIndex
£18.89
Cambridge University Press Inference and Learning from Data Volume 1
Book SynopsisWritten in an engaging and rigorous style by a world authority in the field, this is an accessible and comprehensive introduction to core topics in inference and learning. With downloadable Matlab code and solutions for instructors, this is the ideal introduction for students of data science, machine learning, and engineering.Trade Review'Inference and Learning from Data is a uniquely comprehensive introduction to the signal processing foundations of modern data science. Lucidly written, with a carefully balanced choice of topics, this textbook is an indispensable resource for both graduate students and data science practitioners, a piece of lasting value.' Helmut Bölcskei, ETH Zurich'This textbook provides a lucid and magisterial treatment of methods for inference and learning from data, aided by hundreds of solved examples, computer simulations, and over 1000 problems. The material ranges from fundamentals to recent advances in statistical learning theory; variational inference; neural, convolutional, and Bayesian networks; and several other topics. It is aimed at students and practitioners, and can be used for several different introductory and advanced courses.' Thomas Kailath, Stanford University'A tour de force comprehensive three-volume set for the fast-developing areas of data science, machine learning, and statistical signal processing. With masterful clarity and depth, Sayed covers, connects, and integrates background fundamentals and classical and emerging methods in inference and learning. The books are rich in worked-out examples, exercises, and links to data sets. Commentaries with historical background and contexts for the topics covered in each chapter are a special feature.' Mostafa Kaveh, University of Minnesota'This is the first of a three-volume series covering from fundamentals to the many various methods in inference and learning from data. Professor Sayed is a prolific author of award-winning books and research papers who has himself contributed significantly to many of the topics included in the series. With his encyclopedic knowledge, his careful attention to detail, and in a very approachable style, this first volume covers the basics of matrix theory, probability and stochastic processes, convex and non-convex optimization, gradient-descent, convergence analysis, and several other advanced topics that will be needed for volume II (Inference) and volume III (Learning). This series, and in particular this volume, will be a must-have for educators, students, researchers, and technologists alike who are pursuing a systematic study, want a quick refresh, or may use it as a helpful reference to learn about these fundamentals.' Jose Moura, Carnegie Mellon University'Volume I of Inference and Learning from Data provides a foundational treatment of one of the most topical aspects of contemporary signal and information processing, written by one of the most talented expositors in the field. It is a valuable resource both as a textbook for students wishing to enter the field and as a reference work for practicing engineers.' Vincent Poor, Princeton University'Inference and Learning from Data, Vol. I: Foundations offers an insightful and well-integrated primer with just the right balance of everything that new graduate students need to put their research on a solid footing. It covers foundations in a modern way - emphasizing the most useful concepts, including proofs, and timely topics which are often missing from introductory graduate texts. All in one beautifully written textbook. An impressive feat! I highly recommend it.' Nikolaos Sidiropoulos, University of Virginia'This exceptional encyclopedic work on learning from data will be the bible of the field for many years to come. Totaling more than 3000 pages, this three-volume book covers in an exhaustive and timely manner the topic of data science, which has become critically important to many areas and lies at the basis of modern signal processing, machine learning, artificial intelligence, and their numerous applications. Written by an authority in the field, the book is really unique in scale and breadth, and it will be an invaluable source of information for students, researchers, and practitioners alike.' Peter Stoica, Uppsala University'Very meticulous, thorough, and timely. This volume is largely focused on optimization, which is so important in the modern-day world of data science, signal processing, and machine learning. The book is classical and modern at the same time - many classical topics are nicely linked to modern topics of current interest. All the necessary mathematical background is covered. Professor Sayed is one of the foremost researchers and educators in the field and the writing style is unhurried and clear with many examples, truly reflecting the towering scholar that he is. This volume is so complete that it can be used for self-study, as a classroom text, and as a timeless research reference.' P. P. Vaidyanathan, Caltech'The book series is timely and indispensable. It is a unique companion for graduate students and early-career researchers. The three volumes provide an extraordinary breadth and depth of techniques and tools, and encapsulate the experience and expertise of a world-class expert in the field. The pedagogically crafted text is written lucidly, yet never compromises rigor. Theoretical concepts are enhanced with illustrative figures, well-thought problems, intuitive examples, datasets, and MATLAB codes that reinforce readers' learning.' Abdelhak Zoubir, TU DarmstadtTable of ContentsContents; Preface; Notation; 1. Matrix theory; 2. Vector differentiation; 3. Random variables; 4. Gaussian distribution; 5. Exponential distributions; 6. Entropy and divergence; 7. Random processes; 8. Convex functions; 9. Convex optimization; 10. Lipschitz conditions; 11. Proximal operator; 12. Gradient descent method; 13. Conjugate gradient method; 14. Subgradient method; 15. Proximal and mirror descent methods; 16. Stochastic optimization; 17. Adaptive gradient methods; 18. Gradient noise; 19. Convergence analysis I: Stochastic gradient algorithms; 20. Convergence analysis II: Stochasic subgradient algorithms; 21: Convergence analysis III: Stochastic proximal algorithms; 22. Variance-reduced methods I: Uniform sampling; 23. Variance-reduced methods II: Random reshuffling; 24. Nonconvex optimization; 25. Decentralized optimization I: Primal methods; 26: Decentralized optimization II: Primal-dual methods; Author index; Subject index.
£80.74
Cambridge University Press Inference and Learning from Data Volume 2
Book SynopsisThis extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This second volume, Inference, builds on the foundational topics established in volume I to introduce students to techniques for inferring unknown variables and quantities, including Bayesian inference, Monte Carlo Markov Chain methods, maximum-likelihood estimation, hidden Markov models, Bayesian networks, and reinforcement learning. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including solutions for instructors), 180 solved examples, almost 200 figures, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Learning, and unique in its scale and depth, this teTrade Review'Inference and Learning from Data is a uniquely comprehensive introduction to the signal processing foundations of modern data science. Lucidly written, with a carefully balanced choice of topics, this textbook is an indispensable resource for both graduate students and data science practitioners, a piece of lasting value.' Helmut Bölcskei, ETH Zurich'This textbook provides a lucid and magisterial treatment of methods for inference and learning from data, aided by hundreds of solved examples, computer simulations, and over 1000 problems. The material ranges from fundamentals to recent advances in statistical learning theory; variational inference; neural, convolutional, and Bayesian networks; and several other topics. It is aimed at students and practitioners, and can be used for several different introductory and advanced courses.' Thomas Kailath, Stanford University'A tour de force comprehensive three-volume set for the fast-developing areas of data science, machine learning, and statistical signal processing. With masterful clarity and depth, Sayed covers, connects, and integrates background fundamentals and classical and emerging methods in inference and learning. The books are rich in worked-out examples, exercises, and links to data sets. Commentaries with historical background and contexts for the topics covered in each chapter are a special feature.' Mostafa Kaveh, University of Minnesota'This is the first of a three-volume series covering from fundamentals to the many various methods in inference and learning from data. Professor Sayed is a prolific author of award-winning books and research papers who has himself contributed significantly to many of the topics included in the series. With his encyclopedic knowledge, his careful attention to detail, and in a very approachable style, this first volume covers the basics of matrix theory, probability and stochastic processes, convex and non-convex optimization, gradient-descent, convergence analysis, and several other advanced topics that will be needed for volume II (Inference) and volume III (Learning). This series, and in particular this volume, will be a must-have for educators, students, researchers, and technologists alike who are pursuing a systematic study, want a quick refresh, or may use it as a helpful reference to learn about these fundamentals.' Jose Moura, Carnegie Mellon University'Volume I of Inference and Learning from Data provides a foundational treatment of one of the most topical aspects of contemporary signal and information processing, written by one of the most talented expositors in the field. It is a valuable resource both as a textbook for students wishing to enter the field and as a reference work for practicing engineers.' Vincent Poor, Princeton University'Inference and Learning from Data, Vol. I: Foundations offers an insightful and well-integrated primer with just the right balance of everything that new graduate students need to put their research on a solid footing. It covers foundations in a modern way - emphasizing the most useful concepts, including proofs, and timely topics which are often missing from introductory graduate texts. All in one beautifully written textbook. An impressive feat! I highly recommend it.' Nikolaos Sidiropoulos, University of Virginia'This exceptional encyclopedic work on learning from data will be the bible of the field for many years to come. Totaling more than 3000 pages, this three-volume book covers in an exhaustive and timely manner the topic of data science, which has become critically important to many areas and lies at the basis of modern signal processing, machine learning, artificial intelligence, and their numerous applications. Written by an authority in the field, the book is really unique in scale and breadth, and it will be an invaluable source of information for students, researchers, and practitioners alike.' Peter Stoica, Uppsala University'Very meticulous, thorough, and timely. This volume is largely focused on optimization, which is so important in the modern-day world of data science, signal processing, and machine learning. The book is classical and modern at the same time - many classical topics are nicely linked to modern topics of current interest. All the necessary mathematical background is covered. Professor Sayed is one of the foremost researchers and educators in the field and the writing style is unhurried and clear with many examples, truly reflecting the towering scholar that he is. This volume is so complete that it can be used for self-study, as a classroom text, and as a timeless research reference.' P. P. Vaidyanathan, Caltech'The book series is timely and indispensable. It is a unique companion for graduate students and early-career researchers. The three volumes provide an extraordinary breadth and depth of techniques and tools, and encapsulate the experience and expertise of a world-class expert in the field. The pedagogically crafted text is written lucidly, yet never compromises rigor. Theoretical concepts are enhanced with illustrative figures, well-thought problems, intuitive examples, datasets, and MATLAB codes that reinforce readers' learning.' Abdelhak Zoubir, TU DarmstadtTable of ContentsPreface; Notation; 27. Mean-Square-Error inference; 28. Bayesian inference; 29. Linear regression; 30. Kalman filter; 31. Maximum likelihood; 32. Expectation maximization; 33. Predictive modeling; 34. Expectation propagation; 35. Particle filters; 36. Variational inference; 37. Latent Dirichlet allocation; 38. Hidden Markov models; 39. Decoding HMMs; 40. Independent component analysis; 41. Bayesian networks; 42. Inference over graphs; 43. Undirected graphs; 44. Markov decision processes; 45. Value and policy iterations; 46. Temporal difference learning; 47. Q-learning; 48. Value function approximation; 49. Policy gradient methods; Author index; Subject index.
£74.99
Cambridge University Press Inference and Learning from Data Volume 3
Book SynopsisThis extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This final volume, Learning, builds on the foundational topics established in volume I to provide a thorough introduction to learning methods, addressing techniques such as least-squares methods, regularization, online learning, kernel methods, feedforward and recurrent neural networks, meta-learning, and adversarial attacks. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including complete solutions for instructors), 280 figures, 100 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Inference, and unique in its scale and depth, this textbookTrade Review'Inference and Learning from Data is a uniquely comprehensive introduction to the signal processing foundations of modern data science. Lucidly written, with a carefully balanced choice of topics, this textbook is an indispensable resource for both graduate students and data science practitioners, a piece of lasting value.' Helmut Bölcskei, ETH Zurich'This textbook provides a lucid and magisterial treatment of methods for inference and learning from data, aided by hundreds of solved examples, computer simulations, and over 1000 problems. The material ranges from fundamentals to recent advances in statistical learning theory; variational inference; neural, convolutional, and Bayesian networks; and several other topics. It is aimed at students and practitioners, and can be used for several different introductory and advanced courses.' Thomas Kailath, Stanford University'A tour de force comprehensive three-volume set for the fast-developing areas of data science, machine learning, and statistical signal processing. With masterful clarity and depth, Sayed covers, connects, and integrates background fundamentals and classical and emerging methods in inference and learning. The books are rich in worked-out examples, exercises, and links to data sets. Commentaries with historical background and contexts for the topics covered in each chapter are a special feature.' Mostafa Kaveh, University of Minnesota'This is the first of a three-volume series covering from fundamentals to the many various methods in inference and learning from data. Professor Sayed is a prolific author of award-winning books and research papers who has himself contributed significantly to many of the topics included in the series. With his encyclopedic knowledge, his careful attention to detail, and in a very approachable style, this first volume covers the basics of matrix theory, probability and stochastic processes, convex and non-convex optimization, gradient-descent, convergence analysis, and several other advanced topics that will be needed for volume II (Inference) and volume III (Learning). This series, and in particular this volume, will be a must-have for educators, students, researchers, and technologists alike who are pursuing a systematic study, want a quick refresh, or may use it as a helpful reference to learn about these fundamentals.' Jose Moura, Carnegie Mellon University'Volume I of Inference and Learning from Data provides a foundational treatment of one of the most topical aspects of contemporary signal and information processing, written by one of the most talented expositors in the field. It is a valuable resource both as a textbook for students wishing to enter the field and as a reference work for practicing engineers.' Vincent Poor, Princeton University'Inference and Learning from Data, Vol. I: Foundations offers an insightful and well-integrated primer with just the right balance of everything that new graduate students need to put their research on a solid footing. It covers foundations in a modern way - emphasizing the most useful concepts, including proofs, and timely topics which are often missing from introductory graduate texts. All in one beautifully written textbook. An impressive feat! I highly recommend it.' Nikolaos Sidiropoulos, University of Virginia'This exceptional encyclopedic work on learning from data will be the bible of the field for many years to come. Totaling more than 3000 pages, this three-volume book covers in an exhaustive and timely manner the topic of data science, which has become critically important to many areas and lies at the basis of modern signal processing, machine learning, artificial intelligence, and their numerous applications. Written by an authority in the field, the book is really unique in scale and breadth, and it will be an invaluable source of information for students, researchers, and practitioners alike.' Peter Stoica, Uppsala University'Very meticulous, thorough, and timely. This volume is largely focused on optimization, which is so important in the modern-day world of data science, signal processing, and machine learning. The book is classical and modern at the same time - many classical topics are nicely linked to modern topics of current interest. All the necessary mathematical background is covered. Professor Sayed is one of the foremost researchers and educators in the field and the writing style is unhurried and clear with many examples, truly reflecting the towering scholar that he is. This volume is so complete that it can be used for self-study, as a classroom text, and as a timeless research reference.' P. P. Vaidyanathan, Caltech'The book series is timely and indispensable. It is a unique companion for graduate students and early-career researchers. The three volumes provide an extraordinary breadth and depth of techniques and tools, and encapsulate the experience and expertise of a world-class expert in the field. The pedagogically crafted text is written lucidly, yet never compromises rigor. Theoretical concepts are enhanced with illustrative figures, well-thought problems, intuitive examples, datasets, and MATLAB codes that reinforce readers' learning.' Abdelhak Zoubir, TU DarmstadtTable of ContentsPreface; Notation; 50. Least-squares problems; 51. Regularization; 52. Nearest-neighbor rule; 53. Self-organizing maps; 54. Decision trees; 55. Naive Bayes classifier; 56. Linear discriminant analysis; 57. Principal component analysis; 58. Dictionary learning; 59. Logistic regression; 60. Perceptron; 61. Support vector machines; 62. Bagging and boosting; 63. Kernel methods; 64. Generalization theory; 65. Feedforward neural networks; 66. Deep belief networks; 67. Convolutional networks; 68. Generative networks; 69. Recurrent networks; 70. Explainable learning; 71. Adversarial attacks; 72. Meta learning; Author index; Subject index.
£74.99
Cambridge University Press Introduction to Digital Communications
Book SynopsisMaster the fundamentals of digital communications systems with this accessible and hands-on introductory textbook, carefully interweaving theory and practice. The just-in-time approach introduces essential background as needed, keeping academic theory firmly linked to practical applications. The example-led teaching frames key concepts in the context of real-world systems, such as 5G, WiFi, and GPS. Stark provides foundational material on the trade-offs between energy and bandwidth efficiency, giving students a solid grounding in the fundamental challenges of designing digital communications systems. Features include over 300 illustrative figures, 80 examples, and 130 end-of-chapter problems to reinforce student understanding, with solutions for instructors. Accompanied online by lecture slides, computational MATLAB and Python resources, and supporting data sets, this is the ideal introduction to digital communications for senior undergraduate and graduate students in electrical engineering.Trade Review'This book emphasizes the fundamentals of digital communication as well as its practice. It provides examples to enhance the understanding, and the many illustrations explain the basic concepts very well. Several concepts from actual engineering practice are discussed in detail.' Ender Ayanoglu, University of California, Irvine'Wayne Stark is a widely respected researcher in digital communications, as well as a dedicated and talented teacher. This book reflects his years of experience teaching a challenging and rapidly changing subject to senior undergraduate and first-year graduate students. His choice of topics and careful balance between theory and practice ensure that this book will be a valuable resource in electrical engineering curricula for years to come.' Tom Fuja, University of Notre Dame'This self-contained book is excellent for a first course in digital communications. It strikes a perfect balance in theory, practice, and insights, so that a beginner can get a good understanding without getting lost in advanced mathematical concepts.' Sudharman K. Jayaweera, University of New Mexico'This is an extraordinary textbook on digital communication theory and practices. Key results are derived step by step, and it provides many examples and figures that help students grasp key concepts. I wish it had been available when I was a student.' Sang Wu Kim, Iowa State University'Not only is this textbook comprehensive and well written, it is mathematically rigorous. The specific numerical examples and practical applications enhance the theoretical derivations. The author does an excellent job of communicating the importance of each result, making it an appropriate textbook for senior undergraduates taking a solid course in the theory of digital communications.' Laurence B. Milstein, University of California, San Diego'I enjoyed this book's clarity and logical presentation. It is easy to read, balancing mathematical fundamentals with practical applications, problem sets, and examples. I'd be delighted to use it when teaching my undergraduate course on Communication Systems and Principles. This concise resource provides a thorough foundation on digital communication concepts, systems, and techniques, explaining communication systems in general and digital communications specifically.' Lina Mohjazi, University of Glasgow'The real jewel of the book is the introduction chapter. It lays out the most important design considerations and trade-offs at a high (but not superficial) level straightaway, serving as a roadmap to the material in the rest of the book. It is the best and most useful introduction chapter that no one should skip!' Tan F. Wong, University of Florida'This is an excellent textbook for students, communications engineers, and researchers alike. Based on many years' teaching experience, it includes detailed and illustrative examples that help students understand the fundamentals of digital communications. Professor Stark explains the trade-offs of different key parameters in digital communications, and covers state-of-the-art technologies such as LDPC codes. Each chapter contains clear goals, summaries, and useful exercises.' Xiang-Gen Xia, University of DelawareTable of ContentsContents; Preface; Acknowledgement; List of abbreviations; 1. Fundamentals of digital communications; 2. Modulation and demodulation; 3. Probability, random variables, random processes, signal bandwidth; 4. Error probability for binary signals; 5. Optimal receivers for M-ary communication; 6. Modulation techniques; 7. Wireless channels and transmission techniques; 8. Block codes; 9. Convolutional codes; Appendix A. Pseudorandom sequences; Appendix B. Trigonometric and fourier transform iIdentities; Appendix C. Finite fields and BCH codes; Appendix D. Simulation of signals and noise; References; Index.
£74.99
Cambridge University Press Random Graphs and Networks
Book SynopsisBased on the authors' own teaching experience, this text introduces random graphs and networks, covering all the basic features before discussing the growth and structure of real-world networks. It can be used as a textbook for a one-semester course at advanced undergraduate or graduate level.Trade Review'Random Graphs and Networks: A First Course' is a wonderful textbook that covers a remarkable set of topics written by two leading experts in the field. The textbook is comprehensive and contains a wealth of theoretical preliminaries, exercises and problems, making it ideal for an introductory course or for self-study. It is the best starting point in the present textbook market for any university student interested in the foundations of network science.' Charalampos E. Tsourakakis, Boston UniversityTable of ContentsConventions/Notation; Part I. Preliminaries: 1. Introduction; 2. Basic tools; Part II. Erdos–Rényi–Gilbert Model: 3. Uniform and binomial random graphs; 4. Evolution; 5. Vertex degrees; 6. Connectivity; 7. Small subgraphs; 8. Large subgraphs; 9. Extreme characteristics; Part III. Modeling Complex Networks: 10. Inhomogeneous graphs; 11. Small world; 12. Network processes; 13. Intersection graphs; 14. Weighted graphs; References; Author index; Main index.
£37.99
Taylor & Francis Ltd Uncertainty Quantification in Variational
Book SynopsisUncertainty Quantification (UQ) is an emerging and extremely active research discipline which aims to quantitatively treat any uncertainty in applied models. The primary objective of Uncertainty Quantification in Variational Inequalities: Theory, Numerics, and Applications is to present a comprehensive treatment of UQ in variational inequalities and some of its generalizations emerging from various network, economic, and engineering models. Some of the developed techniques also apply to machine learning, neural networks, and related fields.Features First book on UQ in variational inequalities emerging from various network, economic, and engineering models Completely self-contained and lucid in style Aimed for a diverse audience including applied mathematicians, engineers, economists, and professionals from academia Includes the most recent developments on the subject which so far have only been
£43.69
O'Reilly Media Effective Machine Learning Teams
Book Synopsis
£47.99
Cambridge University Press A Students Guide to Coding and Information Theory
Book SynopsisA concise, easy-to-read guide, introducing beginners to the engineering background of modern communication systems, from mobile phones to data storage. Assuming only basic knowledge of high-school mathematics and including many practical examples and exercises to aid understanding, this is ideal for anyone who needs a quick introduction to the subject.Trade Review'The book is nicely written, and is recommended as a textbook for a one-semester introductory course on coding and information theory.' Pushpa N. Rathie, Zentralblatt MATHTable of Contents1. Introduction Chung-Hsuan Wang; 2. Error-detecting codes Chung-Hsuan Wang; 3. Repetition and hamming codes Francis Lu; 4. Data compression: efficient coding of a random message; 5. Entropy and Shannon's source coding theorem; 6. Mutual information and channel capacity Jwo-Yuh Wu; 7. Achieving the Shannon limit by turbo coding; 8. Other aspects of coding theory Francis Lu.
£68.00
Cambridge University Press Probability The Classical Limit Theorems
Book SynopsisProbability theory has been extraordinarily successful at describing a variety of phenomena, from the behaviour of gases to the transmission of messages, and is, besides, a powerful tool with applications throughout mathematics. At its heart are a number of concepts familiar in one guise or another to many: Gauss' bell-shaped curve, the law of averages, and so on, concepts that crop up in so many settings they are in some sense universal. This universality is predicted by probability theory to a remarkable degree. This book explains that theory and investigates its ramifications. Assuming a good working knowledge of basic analysis, real and complex, the author maps out a route from basic probability, via random walks, Brownian motion, the law of large numbers and the central limit theorem, to aspects of ergodic theorems, equilibrium and nonequilibrium statistical mechanics, communication over a noisy channel, and random matrices. Numerous examples and exercises enrich the text.Trade Review'… packs a great deal of material into a moderate-sized book, starting with a synopsis of measure theory and ending with a taste of current research into random matrices and number theory. The book ranges more widely than the title might suggest … There are numerous exercises sprinkled throughout the book. Most of these are exhortations to fill in details left out of the main discussion or illustrative examples. The exercises are a natural part of the book, unlike the exercises in so many books that were apparently grafted on after-the-fact at a publisher's insistence. McKean has worked in probability and related areas since obtaining his PhD under William Feller in 1955. His book contains invaluable insights from a long career.' John D. Cook, MAA Reviews'The scope is wide, not restricted to 'elementary facts' only. There is an abundance of pretty details … This book is highly recommendable …' Jorma K. Merikoski, International Statistical ReviewTable of ContentsPreface; 1. Preliminaries; 2. Bernoulli trials; 3. The standard random walk; 4. The standard random walk in higher dimensions; 5. LLN, CLT, iterated log, and arcsine in general; 6. Brownian motion; 7. Markov chains; 8. The ergodic theorem; 9. Communication over a noisy channel; 10. Equilibrium statistical mechanics; 11. Statistical mechanics out of equilibrium; 12. Random matrices; Bibliography; Index.
£133.95
Cambridge University Press Quantum Information Theory
Book SynopsisThis new edition of Wilde's popular book promises over 100 pages of new material, exercises and references. New attention is given to the derivation of the Choi-Kraus theorem for quantum channels, the CHSH game, quantum relative entropy, and sequential decoding. The text offers an ideal entry point into the topic for graduate students.Trade Review'For years, I have been hoping that somebody would write a book on quantum information theory that was clear, comprehensive, and up to date. This is that book. And the second edition is even better than the first.' Peter Shor, Massachusetts Institute of Technology'Mark M. Wilde's Quantum Information Theory is a natural expositor's labor of love. Accessible to anyone comfortable with linear algebra and elementary probability theory, Wilde's book brings the reader to the forefront of research in the quantum generalization of Shannon's information theory. What had been a gaping hole in the literature has been replaced by an airy edifice, scalable with the application of reasonable effort and complete with fine vistas of the landscape below. Wilde's book has a permanent place not just on my bookshelf but on my desk.' Patrick Hayden, Stanford University, CaliforniaReview of previous edition: '… [its] clear, thorough, and above all self-contained presentation will aid quantum information researchers in coming up to speed with the latest results in this area of the field. Meanwhile, the familiar setting and language will help classical information theorists who wish to become more acquainted with the quantum aspects of information processing … The presentation is well-structured, making it easy to jump to the desired topic and quickly determine on what that topic depends and how it is used going forward … Quantum Information Theory fills an important gap in the existing literature and will, I expect, help propagate the latest and greatest results in quantum Shannon theory to both quantum and classical researchers.' Joseph M. Renes, Quantum Information ProcessingReview of previous edition: '… a modern self-contained text … suitable for graduate-level courses leading up to research level.' Journal of Discrete Mathematical Sciences and CryptographyReview of previous edition: '… the book does a phenomenal job of introducing, developing and nurturing a mathematical sense of quantum information processing … In a nutshell, this is an essential reference for students and researchers who work in the area or are trying to understand what it is that quantum information theorists study. Wilde, as mentioned in his book, beautifully illustrates 'the ultimate capability of noisy physical systems, governed by the laws of quantum mechanics, to preserve information and correlations' through this book. I would strongly recommend it to anyone who plans to continue working in the field of quantum information.' Subhayan Roy Moulick, SIGCAT NewsTable of ContentsPreface to the second edition; Preface to the first edition; How to use this book; Part I. Introduction: 1. Concepts in quantum Shannon theory; 2. Classical Shannon theory; Part II. The Quantum Theory: 3. The noiseless quantum theory; 4. The noisy quantum theory; 5. The purified quantum theory; Part III. Unit Quantum Protocols: 6. Three unit quantum protocols; 7. Coherent protocols; 8. Unit resource capacity region; Part IV. Tools of Quantum Shannon Theory: 9. Distance measures; 10. Classical information and entropy; 11. Quantum information and entropy; 12. Quantum entropy inequalities and recoverability; 13. The information of quantum channels; 14. Classical typicality; 15. Quantum typicality; 16. The packing lemma; 17. The covering lemma; Part V. Noiseless Quantum Shannon Theory: 18. Schumacher compression; 19. Entanglement manipulation; Part VI. Noisy Quantum Shannon Theory: 20. Classical communication; 21. Entanglement-assisted classical communication; 22. Coherent communication with noisy resources; 23. Private classical communication; 24. Quantum communication; 25. Trading resources for communication; 26. Summary and outlook; Appendix A. Supplementary results; Appendix B. Unique linear extension of a quantum physical evolution; References; Index.
£63.99
Cambridge University Press Probability The Classical Limit Theorems
Book SynopsisProbability theory has been extraordinarily successful at describing a variety of phenomena, from the behaviour of gases to the transmission of messages, and is, besides, a powerful tool with applications throughout mathematics. At its heart are a number of concepts familiar in one guise or another to many: Gauss' bell-shaped curve, the law of averages, and so on, concepts that crop up in so many settings they are in some sense universal. This universality is predicted by probability theory to a remarkable degree. This book explains that theory and investigates its ramifications. Assuming a good working knowledge of basic analysis, real and complex, the author maps out a route from basic probability, via random walks, Brownian motion, the law of large numbers and the central limit theorem, to aspects of ergodic theorems, equilibrium and nonequilibrium statistical mechanics, communication over a noisy channel, and random matrices. Numerous examples and exercises enrich the text.Trade Review'… packs a great deal of material into a moderate-sized book, starting with a synopsis of measure theory and ending with a taste of current research into random matrices and number theory. The book ranges more widely than the title might suggest … There are numerous exercises sprinkled throughout the book. Most of these are exhortations to fill in details left out of the main discussion or illustrative examples. The exercises are a natural part of the book, unlike the exercises in so many books that were apparently grafted on after-the-fact at a publisher's insistence. McKean has worked in probability and related areas since obtaining his PhD under William Feller in 1955. His book contains invaluable insights from a long career.' John D. Cook, MAA Reviews'The scope is wide, not restricted to 'elementary facts' only. There is an abundance of pretty details … This book is highly recommendable …' Jorma K. Merikoski, International Statistical ReviewTable of ContentsPreface; 1. Preliminaries; 2. Bernoulli trials; 3. The standard random walk; 4. The standard random walk in higher dimensions; 5. LLN, CLT, iterated log, and arcsine in general; 6. Brownian motion; 7. Markov chains; 8. The ergodic theorem; 9. Communication over a noisy channel; 10. Equilibrium statistical mechanics; 11. Statistical mechanics out of equilibrium; 12. Random matrices; Bibliography; Index.
£50.56
Cambridge University Press A First Course in Network Science
Book SynopsisNetworks are everywhere: networks of friends, transportation networks and the Web. Neurons in our brains and proteins within our bodies form networks that determine our intelligence and survival. This modern, accessible textbook introduces the basics of network science for a wide range of job sectors from management to marketing, from biology to engineering, and from neuroscience to the social sciences. Students will develop important, practical skills and learn to write code for using networks in their areas of interest - even as they are just learning to program with Python. Extensive sets of tutorials and homework problems provide plenty of hands-on practice and longer programming tutorials online further enhance students'' programming skills. This intuitive and direct approach makes the book ideal for a first course, aimed at a wide audience without a strong background in mathematics or computing but with a desire to learn the fundamentals and applications of network science.Trade Review'A First Course in Network Science by Menczer, Fortunato, and Davis is an easy-to-follow introduction into network science. An accessible text by some of the best-known practitioners of the field, offering a wonderful place to start one's journey into this fascinating field, and its potential applications.' Albert-László Barabási, Dodge Distinguished Professor of Network Science, Northeastern University'… this textbook has finally allowed me to teach the ideal intro courses on network science, of interest to computer scientists as well as mathematicians, statisticians, economists, sociologists, and physicists.' Giancarlo Ruffo, Associate Professor of Computer Science, University of Torino'The book by Menczer, Fortunato, and Davis, A First Course in Network Science, is an amazing tour de force in bringing network science concepts to the layman. It is an extraordinary book with which to start thinking about networks that nowadays represent the linchpins of our world.' Alex Arenas, Universidad Rovira i Virgili'Buckle up! This book bounds ahead of the curve in teaching network science. Without formalism, but with remarkable clarity and insight, the authors use experiential learning to animate concepts, captivate students, and deliver skills for analyzing and simulating network data. This book will not only make students smarter, they will feel and act smarter.' Brian Uzzi, Northwestern University'If you are looking for a sophisticated yet introductory book on network analysis from a network science perspective, look no further. This is an excellent introduction that is also eminently practical, integrating exactly the right set of tools. I highly recommend it.' Stephen Borgatti, University of Kentucky'This is a book that truly takes in hand students from all backgrounds to discover the power of network science. It guides the readers through the basic concepts needed to enter the field, while providing at the same time the necessary programming rudiments and tools. Rigorous, albeit very accessible, this book is the ideal starting point for any student fascinated by the emerging field of network science.' Alessandro Vespignani, Northeastern University'We cannot make sense of the world without learning about networks. This comprehensive and yet accessible text is an essential resource for all interested in mastering the basics of network science. Indispensable for undergraduate and graduate education, the book is also a much-needed primer for researchers across the many disciplines where networks are on the rise.' Olaf Sporns, Indiana University'This is a timely book that comes from authorities in the field of Complex Networks. The book is very well written and represents the state of the art of research in the field. For these reasons, it represents both a reference guide for experts and a great textbook for the students.' Guido Caldarelli, Scuola IMT Alti Studi Lucca'Should be titled the 'Joy of Networks', clearly conveys the fun and power of the science of networks, while providing extensive hands-on exercises with network data.' David Lazer, University Distinguished Professor of Political Science and Computer and Information Science, Northeastern UniversityTable of ContentsPreface; Introduction; 1. Network elements; 2. Small worlds; 3. Hubs; 4. Directions and weights; 5. Network models; 6. Communities; 7. Dynamics; Appendix A. Python tutorial; Appendix B. NetLogo models; Bibliography; Index.
£37.04
Cambridge University Press Machine Learning Refined
Book SynopsisWith its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for gradTrade Review'An excellent book that treats the fundamentals of machine learning from basic principles to practical implementation. The book is suitable as a text for senior-level and first-year graduate courses in engineering and computer science. It is well organized and covers basic concepts and algorithms in mathematical optimization methods, linear learning, and nonlinear learning techniques. The book is nicely illustrated in multiple colors and contains numerous examples and coding exercises using Python.' John G. Proakis, University of California, San Diego'Some machine learning books cover only programming aspects, often relying on outdated software tools; some focus exclusively on neural networks; others, solely on theoretical foundations; and yet more books detail advanced topics for the specialist. This fully revised and expanded text provides a broad and accessible introduction to machine learning for engineering and computer science students. The presentation builds on first principles and geometric intuition, while offering real-world examples, commented implementations in Python, and computational exercises. I expect this book to become a key resource for students and researchers.' Osvaldo Simeone, Kings College London'This book is great for getting started in machine learning. It builds up the tools of the trade from first principles, provides lots of examples, and explains one thing at a time at a steady pace. The level of detail and runnable code show what's really going when we run a learning algorithm.' David Duvenaud, University of Toronto'This book covers various essential machine learning methods (e.g., regression, classification, clustering, dimensionality reduction, and deep learning) from a unified mathematical perspective of seeking the optimal model parameters that minimize a cost function. Every method is explained in a comprehensive, intuitive way, and mathematical understanding is aided and enhanced with many geometric illustrations and elegant Python implementations.' Kimiaki Sihrahama, Kindai University, Japan'Books featuring machine learning are many, but those which are simple, intuitive, and yet theoretical are extraordinary 'outliers'. This book is a fantastic and easy way to launch yourself into the exciting world of machine learning, grasp its core concepts, and code them up in Python or Matlab. It was my inspiring guide in preparing my 'Machine Learning Blinks' on my BASIRA YouTube channel for both undergraduate and graduate levels.' Islem Rekik, Director of the Brain And SIgnal Research and Analysis (BASIRA) Laboratory'With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for graduate courses on machine learning and for individual reference and self-study.' politcommerce.com'This is a comprehensive textbook on the fundamental concepts of machine learning. In the second edition, the authors provide a very accessible introduction to the main ideas behind machine learning models.' Helena Mihaljević, zbMATHTable of Contents1. Introduction to machine learning; Part I. Mathematical Optimization: 2. Zero order optimization techniques; 3. First order methods; 4. Second order optimization techniques; Part II. Linear Learning: 5. Linear regression; 6. Linear two-class classification; 7. Linear multi-class classification; 8. Linear unsupervised learning; 9. Feature engineering and selection; Part III. Nonlinear Learning: 10. Principles of nonlinear feature engineering; 11. Principles of feature learning; 12. Kernel methods; 13. Fully-connected neural networks; 14. Tree-based learners; Part IV. Appendices: Appendix A. Advanced first and second order optimization methods; Appendix B. Derivatives and automatic differentiation; Appendix C. Linear algebra.
£55.09
Cambridge University Press Model Checking Quantum Systems
Book SynopsisModel checking is one of the most successful verification techniques and has been widely adopted in traditional computing and communication hardware and software industries. This book provides the first systematic introduction to model checking techniques applicable to quantum systems, with broad potential applications in the emerging industry of quantum computing and quantum communication as well as quantum physics. Suitable for use as a course textbook and for self-study, graduate and senior undergraduate students will appreciate the step-by-step explanations and the exercises included. Researchers and engineers in the related fields can further develop these techniques in their own work, with the final chapter outlining potential future applications.Trade Review'This book gives a thorough account of the principles of model checking for quantum systems. It covers the basics of verifying qualitative properties such as reachability as well as quantitative properties on quantum Markov chains. This is the first comprehensive work on this young and exciting research field.' Joost-Pieter Katoen, RWTH Aachen University'The authors have been, from the start of the quantum computer science endeavour, at the forefront of research in logical methods for quantum computing. This book provides the best possible introduction to quantum model checking, by the pioneers of the field. Bob Coecke, University of Oxford'A brief final chapter offering conclusions and future prospects will be of wider interest. This work is intended as an introduction for researchers entering the field of quantum computing, and is suitable as a textbook for physics or computer science graduate students … Recommended.' M. C. Ogilvie, Choice MagazineTable of Contents1. Introduction; 2. Basics of Model Checking; 3. Basics of Quantum Theory; 4. Model Checking; 5. Model Checking Quantum Markov Chains; 6. Model Checking Super-operator-valued Markov Chains; 7. Conclusions and Prospects.
£55.99
