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49413 results in Computer Science

Page 1 of 2471

The Cambridge Handbook of the Law of Networks, Platforms and Utilities

Empathy and Artificial Intelligence
  • Challenges, Advances and Ethical Considerations
  • Edited by Anat Perry, C. Daryl Cameron
  • Coming soon
  • Expected online publication date:
    September 2026
    Print publication:
    30 September 2026

Our Artificial Future
  • What Generative AI Means for Law and Society
  • David Atkinson
  • Coming soon
  • Expected online publication date:
    September 2026
    Print publication:
    30 September 2026

Data Analytics using Python

Accountable Design
  • Bringing Law into Human-Centred Computing
  • Lachlan David Urquhart
  • Coming soon
  • Expected online publication date:
    August 2026
    Print publication:
    31 August 2026
  • In the face of the everchanging and increasingly complex regulatory and socio-technical challenges posed by AI and the Internet of Things, there is an urgent need for closer collaboration between technology designers and lawyers. Accountable Design provides a timely framework for bridging disciplines to design legally accountable technologies. Proposing the new concept of Accountable Design, Lachlan David Urquhart explores how to incorporate legal values into human-centered design processes. Three novel case studies ground discussion by showcasing uses of new technologies in cities, homes, and biometric applications while exploring how to design for privacy, security, trust, and safety. The book synthesizes insights from across technology law, human-computer-interaction, design research, science and technology studies, and philosophy of technology to address the challenges of building better technological design futures for humans and society.

Operationalizing Responsible AI
  • Edited by Daniele Quercia, Marios Constantinides
  • Coming soon
  • Expected online publication date:
    August 2026
    Print publication:
    31 August 2026
  • How can we build and govern trustworthy AI? Operationalizing Responsible AI brings together leading scholars and practitioners to address this urgent question. Each chapter explores a key dimension of responsibility - fairness, explainability, psychological safety, accountability, consent, transparency, auditability, and contextualization – defining what it means, why it matters, and how it can be achieved in practice. Through interdisciplinary perspectives and real-world examples, the book bridges ethical principles, legal frameworks such as the EU AI Act, and technical approaches including explainable AI and audit methodologies. Written for researchers, policymakers, and professionals, the book offers both conceptual clarity and practical guidance for advancing Responsible AI that is fair, transparent, and aligned with human values.

Computer Networks

Reinforcement Learning Foundations
  • Shie Mannor, Yishay Mansour, Aviv Tamar
  • Coming soon
  • Expected online publication date:
    July 2026
    Print publication:
    31 July 2026
  • Bridging the gap between introductory texts and the specialized research literature, this is one of the first truly rigorous yet accessible treatments of modern reinforcement learning. Written by three leading researchers with over a decade of teaching experience, the book uniquely combines mathematical precision with practical insights. It progresses naturally from planning (dynamic programming, MDPs, value and policy iteration) to learning (model-based and model-free algorithms, function approximation, policy gradients, and regret minimization). Each concept is developed from first principles with complete proofs, making the material self-contained. The modular chapter organization enables flexible course design. The book's website offers battle-tested exercises refined through years of classroom use. Combining mathematical rigor with practical applications, this definitive text is ideal for advanced undergraduate and graduate students as well as practitioners seeking a deep understanding of sequential decision-making and intelligent agent design.

Blockchain Fundamentals, Data Structures, and Algorithms for Data Science
  • Cüneyt Gürcan Akçora, Murat Kantarcioglu, Yulia R. Gel
  • Coming soon
  • Expected online publication date:
    July 2026
    Print publication:
    31 July 2026
  • Offering a systematic exploration of blockchain networks from both technical and analytical viewpoints, this book introduces the core structures that underpin blockchain systems, transactions, addresses, and smart contracts and explains how these can be modeled, visualized, and analyzed using modern data science methods. Bridging computer science, finance, and statistics, it integrates algorithmic reasoning with economic intuition to study decentralization, risk, and trust in digital economies. Through examples drawn from Bitcoin, Ethereum, Ripple, Monero, Zcash, IOTA, and DeFi, readers learn how blockchain data can be transformed into graph and temporal models for fraud detection, systemic risk analysis, and network behavior prediction. Featuring clear explanations, illustrative figures, and Solidity code, this volume serves as an essential reference for students, researchers, and practitioners in finance, data science, statistics, machine learning, and distributed systems.

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.

Generative AI and Stochastic Thermodynamics

Modern Statistical Methods and Theory
  • An Introduction to Nonparametric and High-Dimensional Statistics
  • Richard J. Samworth, Rajen D. Shah
  • Coming soon
  • Expected online publication date:
    July 2026
    Print publication:
    31 July 2026
  • Based on courses taught at the University of Cambridge, this text presents core contemporary statistical methods and theory in an accessible, self-contained and rigorous fashion, with a focus on finite-sample guarantees as opposed to asymptotic arguments. Many of the topics and results have not appeared in book form previously, and some constitute new research. The prerequisites are relatively light (primarily a good grasp of linear algebra and real analysis) and complete solutions to all 250+ exercises are available online. It is the perfect entry point to the subject for master's and graduate-level students in statistics, data science and machine learning, as well as related disciplines such as artificial intelligence, signal processing, information theory, electrical engineering and econometrics. Researchers in these fields will also find it an invaluable resource. 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.

Human-Centered Machine Learning
  • Edited by Rebecca Fiebrink, Marco Gillies, Gonzalo Ramos
  • Coming soon
  • Expected online publication date:
    July 2026
    Print publication:
    31 July 2026
  • This collection of articles and interviews surveys human-centered approaches to machine learning that can make AI more human-friendly, usable, and ethical. It provides a handbook for students, researchers, and practitioners who want new ways of approaching AI that place humanity at their center. It shows how to apply methods from human-computer interaction that have enabled computing technology to become user-friendly and human-centric to the new technologies of AI and machine learning. The book has 13 articles and 9 interviews from a range of different perspectives, helping readers understand existing machine learning systems and their impacts on people and society. It is an ideal introduction both for human-computer interaction practitioners who are interested in working with machine learning and for machine learning experts interested in making their practice more human-centered. The book offers a critical lens on existing machine learning alongside an optimistic vision of AI in the service of humanity.

Translate
  • Multilingual Access to Critical Services in the Age of AI
  • Lucas N. Vieira
  • Coming soon
  • Expected online publication date:
    June 2026
    Print publication:
    30 June 2026
  • Automated translations can break down language barriers and increase access to information, but they can also be highly inaccurate. This timely book explores the social challenges and ethical considerations of using artificial intelligence (AI) translations in high-stakes professional environments. Based on contributions from over two thousand professionals from critical sectors including healthcare, social work, emergency services and the police, the analysis explores the motivations and consequences of multilingual uses of AI across these sectors. Real-life examples provided throughout the book bring home the delicate balance of risks and benefits of using AI to serve and communicate with multilingual communities. By drawing on concepts such as virtue, trust, empathy and AI literacy, this book makes a case for nuance and flexibility, defends the value of language access, and calls for greater transparency in the development and deployment of AI translation tools. This title is also available as open access on Cambridge Core.

Markov Decision Processes and Reinforcement Learning
  • Martin L. Puterman, Timothy C. Y. Chan
  • Coming soon
  • Expected online publication date:
    June 2026
    Print publication:
    30 June 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.

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.

All of Regression
  • Isabella Verdinelli, Larry Wasserman
  • Coming soon
  • Expected online publication date:
    May 2026
    Print publication:
    31 May 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.

Accelerating Deep Neural Networks
  • Ryoma Sato
  • Coming soon
  • Expected online publication date:
    April 2026
    Print publication:
    30 April 2026
  • Deep learning models are powerful, but are 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

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.

Page 1 of 2471