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48746 results in Computer Science

Page 1 of 2438

Computer Networks

Global Governance by Data
  • Infrastructures of Algorithmic Rule
  • Edited by Fleur Johns, Gavin Sullivan, Dimitri Van Den Meerssche
  • Coming soon
  • Expected online publication date:
    July 2026
    Print publication:
    31 July 2026
  • We are living through an era of unprecedented data-driven regulatory transformation. AI and algorithmic governance are rapidly altering how global problems are known and governed, and reconfiguring how people, places, and things are drawn into legal relation across diverse areas - from labour, media and communications, and global mobilities to environmental governance, security, and war. These changes are fostering new forms of power, inequality, and violence, and posing urgent conceptual and methodological challenges for law and technology research. Global Governance by Data: Infrastructures of Algorithmic Rule brings together leading interdisciplinary scholars working at the forefront of creative thinking and research practice in this area. The book offers fresh takes on the prospects for working collectively to critique and renew those legal and technological infrastructures that order, divide, empower and immiserate across our data-driven world. This title is also available as open access on Cambridge Core.

Equalizing Justice
  • Harnessing AI for Litigants Without Lawyers
  • L. Karl Branting
  • With Mia Bonardi
  • Coming soon
  • Expected online publication date:
    July 2026
    Print publication:
    31 July 2026
  • Millions of individuals worldwide struggle to understand and assert their legal rights without legal representation. Equalizing Justice examines how AI and other technologies can address this access to justice crisis by providing unrepresented litigants with knowledge and skills traditionally available only through lawyers. This volume takes a needs-first approach, identifying tasks that unrepresented litigants must complete and mapping specific technologies to each task, such as generative AI, computational logic, and document automation. The book highlights real-world applications, demonstrating proven impact, and presents case studies and interviews to explore both the potential positive outcomes and potential challenges of AI for access to justice. Equalizing Justice proves that AI technologies offer unprecedented opportunities to create equitable justice systems serving everyone, not just those who can afford representation. The volume argues for legal AI assistants as a public good that should be accessible to all.

All of Regression
  • Isabella Verdinelli, Larry Wasserman
  • Coming soon
  • Expected online publication date:
    June 2026
    Print publication:
    30 June 2026
  • This comprehensive modern look at regression covers a wide range of topics and relevant contemporary applications, going well beyond the topics covered in most introductory books. With concision and clarity, the authors present linear regression, nonparametric regression, classification, logistic and Poisson regression, high-dimensional regression, quantile regression, conformal prediction and causal inference. There are also brief introductions to neural nets, deep learning, random effects, survival analysis, graphical models and time series. Suitable for advanced undergraduate and beginning graduate students, the book will also serve as a useful reference for researchers and practitioners in data science, machine learning, and artificial intelligence who want to understand modern methods for data analysis.

Inference in Statistical Modelling and Machine Learning
  • A Concise Introduction
  • James Burridge, Nick Tosh
  • Coming soon
  • Expected online publication date:
    May 2026
    Print publication:
    31 May 2026
  • Statistical modelling and machine learning offer a vast toolbox of inference methods with which to model the world, discover patterns and reach beyond the data to make predictions when the truth is not certain. This concise book provides a clear introduction to those tools and to the core ideas – probabilistic model, likelihood, prior, posterior, overfitting, underfitting, cross-validation – that unify them. A mixture of toy and real examples illustrates diverse applications ranging from biomedical data to treasure hunts, while the accompanying datasets and computational notebooks in R and Python encourage hands-on learning. Instructors can benefit from online lecture slides and exercise solutions. Requiring only first-year university-level knowledge of calculus, probability and linear algebra, the book equips students in statistics, data science and machine learning, as well as those in quantitative applied and social science programmes, with the tools and conceptual foundations to explore more advanced techniques.

Accelerating Deep Neural Networks
  • Ryoma Sato
  • Coming soon
  • Expected online publication date:
    May 2026
    Print publication:
    31 May 2026
  • Deep learning models are powerful, but often large, slow, and expensive to run. This book is a practical guide to accelerating and compressing neural networks using proven techniques such as quantization, pruning, distillation, and fast architectures. It explains how and why these methods work, fostering a comprehensive understanding. Written for engineers, researchers, and advanced students, the book combines clear theoretical insights with hands-on PyTorch implementations and numerical results. Readers will learn how to reduce inference time and memory usage, lower deployment costs, and select the right acceleration strategy for their task. Whether you're working with large language models, vision systems, or edge devices, this book gives you the tools and intuition needed to build faster, leaner AI systems, without sacrificing performance. It is perfect for anyone who wants to go beyond intuition and take a principled approach to optimizing AI systems

Markov Decision Processes and Reinforcement Learning
  • Martin L. Puterman, Timothy C. Y. Chan
  • Coming soon
  • Expected online publication date:
    April 2026
    Print publication:
    30 April 2026
  • This book offers a comprehensive introduction to Markov decision process and reinforcement learning fundamentals using common mathematical notation and language. Its goal is to provide a solid foundation that enables readers to engage meaningfully with these rapidly evolving fields. Topics covered include finite and infinite horizon models, partially observable models, value function approximation, simulation-based methods, Monte Carlo methods, and Q-learning. Rigorous mathematical concepts and algorithmic developments are supported by numerous worked examples. As an up-to-date successor to Martin L. Puterman's influential 1994 textbook, this volume assumes familiarity with probability, mathematical notation, and proof techniques. It is ideally suited for students, researchers, and professionals in operations research, computer science, engineering, and economics.

The Cambridge Handbook of Behavioural Data Science
  • Edited by Ganna Pogrebna, Thomas T. Hills
  • Coming soon
  • Expected online publication date:
    April 2026
    Print publication:
    30 April 2026
  • The Cambridge Handbook of Behavioural Data Science offers an essential exploration of how behavioural science and data science converge to study, predict, and explain human, algorithmic, and systemic behaviours. Bringing together scholars from psychology, economics, computer science, engineering, and philosophy, the Handbook presents interdisciplinary perspectives on emerging methods, ethical dilemmas, and real-world applications. Organised into modular parts-Human Behaviour, Algorithmic Behaviour, Systems and Culture, and Applications—it provides readers with a comprehensive, flexible map of the field. Covering topics from cognitive modelling to explainable AI, and from social network analysis to ethics of large language models, the Handbook reflects on both technical innovations and the societal impact of behavioural data, and reinforces concepts in online supplementary materials and videos. The book is an indispensable resource for researchers, students, practitioners, and policymakers who seek to engage critically and constructively with behavioural data in an increasingly digital and algorithmically mediated world.

Games on Graphs
  • From Logic and Automata to Algorithms
  • Edited by ‪Nathanaël Fijalkow
  • Coming soon
  • Expected online publication date:
    April 2026
    Print publication:
    30 April 2026
  • This volume introduces the fundamental results and the state of the art in infinite duration games on graphs. These versatile game models first appeared in the study of automata and logic, but later became important for program verification and synthesis. They have many more applications. In particular, this book covers models from the fields of optimisation, reinforcement learning, model theory, and set theory. The material is presented at a level accessible to beginning graduate students, requiring only a basic understanding of discrete algorithms and graphs. The book's primary objective is constructing efficient algorithms for analysing different types of games. Rather than describe their implementation in full detail, it investigates their theoretical foundations and proves their properties and winning strategies. This tutorial reference from experts across the field is ideal for students, researchers, and professionals in logic and automata, verification and synthesis, and related fields.

Differential Equations and Variational Methods on Graphs
  • With Applications to Machine Learning and Image Analysis
  • Yves van Gennip, Jeremy Budd
  • Coming soon
  • Expected online publication date:
    April 2026
    Print publication:
    30 April 2026
  • The burgeoning field of differential equations on graphs has experienced significant growth in the past decade, propelled by the use of variational methods in imaging and by its applications in machine learning. This text provides a detailed overview of the subject, serving as a reference for researchers and as an introduction for graduate students wishing to get up to speed. The authors look through the lens of variational calculus and differential equations, with a particular focus on graph-Laplacian-based models and the graph Ginzburg-Landau functional. They explore the diverse applications, numerical challenges, and theoretical foundations of these models. A meticulously curated bibliography comprising approximately 800 references helps to contextualise this work within the broader academic landscape. While primarily a review, this text also incorporates some original research, extending or refining existing results and methods.

The Cambridge Handbook of AI in Civil Dispute Resolution
  • Edited by Amy J. Schmitz, Marco Giacalone, Pietro Ortolani
  • Coming soon
  • Expected online publication date:
    April 2026
    Print publication:
    30 April 2026
  • The Cambridge Handbook of AI in Civil Dispute Resolution is the first global, in-depth exploration of how artificial intelligence is transforming civil justice. Moving past speculation, it showcases real-world applications-from predictive analytics in Brazil's courts to generative AI in the Dutch legal system and China's AI-driven Internet Courts. Leading scholars and practitioners examine the legal, ethical, and regulatory challenges, including the EU AI Act and emerging governance frameworks. With rich case studies and comparative insights, the book explores AI's impact on access to justice, procedural fairness, and the evolving public–private balance. Essential reading for legal academics, policymakers, technologists, and dispute resolution professionals, it offers a critical lens on AI's promise-and its limits-in reshaping civil dispute resolution worldwide.

The Cambridge Handbook of AI and Technologies in Courts
  • Edited by Monika Zalnieriute, Agne Limante
  • Coming soon
  • Expected online publication date:
    April 2026
    Print publication:
    30 April 2026
  • This Handbook is the first global comparative volume that examines the use of AI and digital technologies in courts. With contributions from over seventy academics, judges, and other professionals from over twenty-five countries, it provides an interdisciplinary and cross-jurisdictional perspective on how judicial institutions are responding to the opportunities and risks posed by AI. Covering judicial use of AI across domestic and regional jurisdictions in Europe, North and South America, Asia-Pacific and Africa, this Handbook begins with the premise that introducing AI into courts is not merely a technical upgrade but a constitutional reckoning and fresh call for judicial accountability. Each chapter examines not just what AI can do for courts, but what courts must do to ensure that AI tools enhance, rather than erode judicial values, justice and the rule of law.

Basic Graph Theory
  • Béla Bollobás, Robert Morris
  • Coming soon
  • Expected online publication date:
    April 2026
    Print publication:
    30 April 2026
  • Over the past few decades, graph theory has developed into one of the central areas of modern mathematics, with close (and growing) connections to areas of pure mathematics such as number theory, probability theory, algebra and geometry, as well as to applied areas such as the theory of networks, machine learning, statistical physics, and biology. It is a young and vibrant area, with several major breakthroughs having occurred in just the past few years. This book offers the reader a gentle introduction to the fundamental concepts and techniques of graph theory, covering classical topics such as matchings, colourings and connectivity, alongside the modern and vibrant areas of extremal graph theory, Ramsey theory, and random graphs. The focus throughout is on beautiful questions, ideas and proofs, and on illustrating simple but powerful techniques, such as the probabilistic method, that should be part of every young mathematician's toolkit.

Games, Markets, and Online Learning
  • Christian Kroer
  • Coming soon
  • Expected online publication date:
    March 2026
    Print publication:
    31 March 2026
  • This book presents a modern introduction to the field of algorithmic game theory. It places a heavy emphasis on optimization and online learning (a subdiscipline of machine learning), which are tools that increasingly play a central role in both the theory and practice of applying game-theoretic ideas. The book covers the core techniques used in several majorly successful applications, including techniques used for creating superhuman poker AIs, the theory behind the 'pacing' methodology that has become standard in the internet advertising industry, and the application of competitive equilibrium from equal incomes for fair course seat allocation in many business schools. With its focus on online learning tools, this book is an ideal companion to classic texts on algorithmic game theory for graduate students and researchers.

A Science of Concurrent Programs
  • Leslie Lamport
  • Coming soon
  • Expected online publication date:
    March 2026
    Print publication:
    31 March 2026
  • Turing Award-winner Leslie Lamport shares the key lessons he has learned about concurrent and distributed computing over decades of writing and reasoning about their algorithms. Algorithms are not programs, and they shouldn't be written in a programming language. Instead, this book explores how to write them and reason about them by using mathematics. It explains the principles underlying abstract programs, which should be implemented to avoid concurrency errors. Designing an abstract program before writing any code can lead to better, more reliable programs. The book has very few mathematical prerequisites, with an appendix summarizing the necessary knowledge. The examples are written in TLA+ and are available online. This is a fascinating read for any graduate students and researchers in theoretical computer science, concurrency, and distributed systems.

Knowledge-Infused Learning
  • Neurosymbolic AI for Explainability, Interpretability, and Safety
  • Manas Gaur, Amit P. Sheth
  • Coming soon
  • Expected online publication date:
    March 2026
    Print publication:
    31 March 2026
  • Knowledge-infused learning directly confronts the opacity of current 'black-box' AI models by combining data-driven machine learning techniques with the structured insights of symbolic AI. This guidebook introduces the pioneering techniques of neurosymbolic AI, which blends statistical models with symbolic knowledge to make AI safer and user-explainable. This is critical in high-stakes AI applications in healthcare, law, finance, and crisis management. The book brings readers up to speed on advancements in statistical AI, including transformer models such as BERT and GPT, and provides a comprehensive overview of weakly supervised, distantly supervised, and unsupervised learning methods alongside their knowledge-enhanced variants. Other topics include active learning, zero-shot learning, and model fusion. Beyond theory, the book presents practical considerations and applications of neurosymbolic AI in conversational systems, mental health, crisis management systems, and social and behavioral sciences, making it a pragmatic reference for AI system designers in academia and industry.

Speech Technology
  • A Theoretical and Practical Introduction
  • Michael Hammond
  • Coming soon
  • Expected online publication date:
    March 2026
    Print publication:
    31 March 2026
  • In recent years, speech recognition devices have become central to our everyday lives. Systems such as Siri, Alexa, speech-to-text, and automated telephone services, are built by people applying expertise in sound structure and natural language processing to generate computer programmes that can recognise and understand speech. This exciting new advancement has led to a rapid growth in speech technology courses being added to linguistics programmes; however, there has so far been a lack of material serving the needs of students who have limited or no background in computer science or mathematics. This textbook addresses that need, by providing an accessible introduction to the fundamentals of computer speech synthesis and automatic speech recognition technology, covering both neural and non-neural approaches. It explains the basic concepts in non-technical language, providing step-by-step explanations of each formula, practical activities and ready-made code for students to use, which is also available on an accompanying website.

Bandit Convex Optimisation
  • Tor Lattimore
  • Coming soon
  • Expected online publication date:
    February 2026
    Print publication:
    28 February 2026
  • This comprehensive reference brings readers to the frontier of research on bandit convex optimization or zeroth-order convex optimization. The focus is on theoretical aspects, with short, self-contained chapters covering all the necessary tools from convex optimization and online learning, including gradient-based algorithms, interior point methods, cutting plane methods and information-theoretic machinery. The book features a large number of exercises, open problems and pointers to future research directions, making it ideal for students as well as researchers.

Extremal Graph and Hypergraph Theory
  • With Ramsey Theory
  • Dhruv Mubayi, Jacques A. Verstraete
  • Coming soon
  • Expected online publication date:
    February 2026
    Print publication:
    28 February 2026
  • Providing a cohesive reference for advanced undergraduates, graduate students and even experienced researchers, this text contains both introductory and advanced material in extremal graph theory, hypergraph theory and Ramsey theory. Along the way, the book includes many modern proof techniques in the field such as the probabilistic method and algebraic methods. Several recent breakthroughs are presented with complete proofs, for example, recent results on the sunflower problem, and off-diagonal and geometric Ramsey theory. It is perhaps unique in containing material on both hypergraph regularity and containers. Featuring an extensive list of exercises, the text is suitable as a teaching text for a variety of courses in extremal combinatorics. Each of the two parts can form the basis of separate courses, and the majority of sections are designed to match the length of a single lecture.

Formal Methods, Informally
  • How to Write Programs That Work
  • Carroll Morgan
  • Coming soon
  • Expected online publication date:
    February 2026
    Print publication:
    31 January 2026
  • Learn to program more effectively, faster, with better results… and enjoy both the learning experience and the benefits it ultimately brings. While this undergraduate-level textbook is motivated by formal methods, so encouraging habits that lead to correct and concise computer programs, its informal presentation sidesteps any rigid reliance on formal logic which programmers are sometimes led to believe is required. Instead, a straightforward and intuitive use of simple 'What's true here?' comments encourages precision of thought without prescription of notation. Drawing on decades of the author's experience in teaching/industry, the text's careful presentation concentrates on key principles of structuring and reasoning about programs, applying them first to small, understandable algorithms. Then students can concentrate on turning those reliably into their corresponding – and correct – program source codes. The text includes over 200 exercises, for many of which full solutions are provided. A set of all solutions is available for instructors' use.

Page 1 of 2438