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48177 results in Computer Science

Page 1 of 2409

Markov Decision Processes and Reinforcement Learning
  • Martin L. Puterman, Timothy C. Y. Chan
  • Coming soon
  • Expected online publication date:
    April 2026
    Print publication:
    01 March 2027
  • 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:
    01 March 2027
  • 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.

Basic Graph Theory

Games, Markets, and Online Learning
  • Christian Kroer
  • Coming soon
  • Expected online publication date:
    March 2026
    Print publication:
    01 March 2027
  • 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.

Governing AI
  • A Primer
  • Onur Bakiner
  • Coming soon
  • Expected online publication date:
    March 2026
    Print publication:
    31 March 2026
  • Governing AI is about getting AI right. Building upon AI scholarship in science and technology studies, technology law, business ethics, and computer science, it documents potential risks and actual harms associated with AI, lists proposed solutions to AI-related problems around the world, and assesses their impact. The book presents a vast range of theoretical debates and empirical evidence to document how and how well technical solutions, business self-regulation, and legal regulation work. It is a call to think inside and outside the box. Technical solutions, business self-regulation, and especially legal regulation can mitigate and even eliminate some of the potential risks and actual harms arising from the development and use of AI. However, the long-term health of the relationship between technology and society depends on whether ordinary people are empowered to participate in making informed decisions to govern the future of technology – AI included.

Extremal Graph and Hypergraph Theory

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:
    February 2026
    Print publication:
    28 February 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.

Move Slow and Upgrade
  • The Power of Incremental Innovation
  • Evan Selinger, Albert Fox Cahn
  • Coming soon
  • Expected online publication date:
    January 2026
    Print publication:
    05 February 2026
  • For far too long, tech titans peddled promises of disruptive innovation - fabricating benefits and minimizing harms. The promise of quick and easy fixes overpowered a growing chorus of critical voices, driving a sea of private and public investments into increasingly dangerous, misguided, and doomed forms of disruption, with the public paying the price. But what's the alternative? Upgrades - evidence-based, incremental change. Instead of continuing to invest in untested, high-risk innovations, constantly chasing outsized returns, upgraders seek a more proven path to proportional progress. This book dives deep into some of the most disastrous innovations of recent years - the metaverse, cryptocurrency, home surveillance, and AI, to name a few - while highlighting some of the unsung upgraders pushing real progress each day. Timely and corrective, Move Slow and Upgrade pushes us past the baseless promises of innovation, towards realistic hope.

One Hundred Years of Game Theory
  • A Nobel Symposium
  • Edited by Mark Voorneveld, Jörgen W. Weibull, Tommy Andersson, Roger Myerson, Jean-François Laslier, Rida Laraki, Yukio Koriyama
  • Coming soon
  • Expected online publication date:
    January 2026
    Print publication:
    31 January 2026
  • In the winter of 2021, the Swedish Nobel Foundation organized a Nobel symposium 'One Hundred Years of Game Theory' to commemorate the publication of famous mathematician Emile Borel's 'La théorie du jeu et les équations intégrales à noyau symétrique'. The symposium gathered roughly forty of the world's most prominent scholars ranging from mathematical foundations to applications in economics, political science, computer science, biology, sociology, and other fields. One Hundred Years of Game Theory brings together their writings to summarize and put in perspective the main achievements of game theory in the last one hundred years. They address past achievements, taking stock of what has been accomplished and contemplating potential future developments and challenges. Offering cross-disciplinary discussions between eminent researchers including five Nobel laureates, one Fields medalist and two Gödel prize winners, the contributors provide a fascinating landscape of game theory and its wide range of applications.

Introduction to Neuromorphic Computing
  • Shriram Ramanathan, Abhronil Sengupta
  • Coming soon
  • Expected online publication date:
    January 2026
    Print publication:
    31 January 2026
  • Artificial intelligence is transforming industries and society, but its high energy demands challenge global sustainability goals. Biological intelligence, in contrast, offers both good performance and exceptional energy efficiency. Neuromorphic computing, a growing field inspired by the structure and function of the brain, aims to create energy-efficient algorithms and hardware by integrating insights from biology, physics, computer science, and electrical engineering. This concise and accessible book delves into the principles, mechanisms, and properties of neuromorphic systems. It opens with a primer on biological intelligence, describing learning mechanisms in both simple and complex organisms, then turns to the application of these principles and mechanisms in the development of artificial synapses and neurons, circuits, and architectures. The text also delves into neuromorphic algorithm design, and the unique challenges faced by algorithmic researchers working in this area. The book concludes with a selection of practice problems, with solutions available to instructors online.

Formal Methods, Informally
  • How to Write Programs That Work
  • Carroll Morgan
  • Coming soon
  • Expected online publication date:
    January 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. This undergraduate-level textbook is motivated by Formal Methods, encouraging habits that lead to correct and concise computer programs; but its informal approach sidesteps reliance on Formal Logic that 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, with full solutions available online for instructors' use, plus mini-projects and automated quizzes to support instructors in building their own courses.

Quantum Computing Unveiled
  • A Concise Course with Topological Extensions
  • Yidun Wan
  • Coming soon
  • Expected online publication date:
    January 2026
    Print publication:
    31 January 2026
  • Aimed at advanced undergraduate and graduate-level students, this textbook covers the core topics of quantum computing in a format designed for a single-semester course. It will be accessible to learners from a range of disciplines, with an understanding of linear algebra being the primary prerequisite. The textbook introduces central concepts such as quantum mechanics, the quantum circuit model, and quantum algorithms, and covers advanced subjects such as the surface code and topological quantum computation. These topics are essential for understanding the role of symmetries in error correction and the stability of quantum architectures, which situate quantum computation within the wider realm of theoretical physics. Graphical representations and exercises are included throughout the book and optional expanded materials are summarized within boxed 'Remarks'. Lecture notes have been made freely available for download from the textbook's webpage, with instructors having additional online access to selected exercise solutions.

Knowledge-Infused Learning
  • Neurosymbolic AI for Explainability, Interpretability, and Safety
  • Manas Gaur, Amit P. Sheth
  • Coming soon
  • Expected online publication date:
    December 2025
    Print publication:
    31 December 2025
  • 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.

The Mathematics of Origami
  • Joseph O'Rourke
  • Coming soon
  • Expected online publication date:
    December 2025
    Print publication:
    31 December 2025
  • When you see a paper crane, what do you think of? A symbol of hope, a delicate craft, The Karate Kid? What you might not see, but is ever present, is the fascinating mathematics underlying it. Origami is increasingly applied to engineering problems, including origami-based stents, deployment of solar arrays in space, architecture, and even furniture design. The topic is actively developing, with recent discoveries at the frontier (e.g., in rigid origami and in curved-crease origami) and an infusion of techniques and algorithms from theoretical computer science. The mathematics is often advanced, but this book instead relies on geometric intuition, making it accessible to readers with only a high school geometry and trigonometry background. Through careful exposition, more than 150 color figures, and 49 exercises all completely solved in an Appendix, the beautiful mathematics leading to stunning origami designs can be appreciated by students, teachers, engineers, and artists alike.

Cultures of Programming
  • The Development of Programming Concepts and Methodologies
  • Tomas Petricek
  • Coming soon
  • Expected online publication date:
    December 2025
    Print publication:
    31 December 2025
  • What defines a correct program? What education makes a good programmer? The answers to these questions depend on whether programs are seen as mathematical entities, engineered socio-technical systems or media for assisting human thought. Programmers have developed a wide range of concepts and methodologies to construct programs of increasing complexity. This book shows how those concepts and methodologies emerged and developed from the 1940s to the present. It follows several strands in the history of programming and interprets key historical moments as interactions between five different cultures of programming. Rooted in disciplines such as mathematics, electrical engineering, business management or psychology, the different cultures of programming have exchanged ideas and given rise to novel programming concepts and methodologies. They have also clashed about the nature of programming; those clashes remain at the core of many questions about programming today. This title is also available as Open Access on Cambridge Core.

Human–Computer Interaction and U.S. Law
  • Jonathan Lazar, Casey Fiesler, Brian Wentz, Raja Kushalnagar
  • Foreword by Lorrie Cranor
  • Coming soon
  • Expected online publication date:
    December 2025
    Print publication:
    31 December 2025
  • The core topics at the intersection of human-computer interaction (HCI) and US law -- privacy, accessibility, telecommunications, intellectual property, artificial intelligence (AI), dark patterns, human subjects research, and voting -- can be hard to understand without a deep foundation in both law and computing. Every member of the author team of this unique book brings expertise in both law and HCI to provide an in-depth yet understandable treatment of each topic area for professionals, researchers, and graduate students in computing and/or law. Two introductory chapters explaining the core concepts of HCI (for readers with a legal background) and U.S. law (for readers with an HCI background) are followed by in-depth discussions of each topic.

Shipping Machine Learning Systems
  • A Practical Guide to Building, Deploying, and Scaling in Production
  • Mohamed El-Geish, Shabaz Patel, Anand Sampat
  • In collaboration with Hira Dangol
  • Coming soon
  • Expected online publication date:
    December 2025
    Print publication:
    31 December 2025
  • This book bridges the gap between theoretical machine learning (ML) and its practical application in industry. It serves as a handbook for shipping production-grade ML systems, addressing challenges often overlooked in academic texts. Drawing on their experience at several major corporations and startups, the authors focus on real-world scenarios, guiding practitioners through the ML lifecycle, from planning and data management to model deployment and optimization. They highlight common pitfalls and offer interview-based case studies from companies that illustrate diverse industrial applications and their unique challenges. Multiple pathways through the book allow readers to choose which stage of the ML development process to focus on, as well as the learning strategy ('crawl,' 'walk,' or 'run') that best suits the needs of their project or team.

Proof Theory and Logic Programming
  • Computation as Proof Search
  • Dale Miller
  • Coming soon
  • Expected online publication date:
    December 2025
    Print publication:
    31 December 2025

Introduction to Online Control
  • Elad Hazan, Karan Singh
  • Coming soon
  • Expected online publication date:
    December 2025
    Print publication:
    31 December 2025
  • This tutorial guide introduces online nonstochastic control, an emerging paradigm in control of dynamical systems and differentiable reinforcement learning that applies techniques from online convex optimization and convex relaxations to obtain new methods with provable guarantees for classical settings in optimal and robust control. In optimal control, robust control, and other control methodologies that assume stochastic noise, the goal is to perform comparably to an offline optimal strategy. In online control, both cost functions and perturbations from the assumed dynamical model are chosen by an adversary. Thus, the optimal policy is not defined a priori and the goal is to attain low regret against the best policy in hindsight from a benchmark class of policies. The resulting methods are based on iterative mathematical optimization algorithms and are accompanied by finite-time regret and computational complexity guarantees. This book is ideal for graduate students and researchers interested in bridging classical control theory and modern machine learning.

Page 1 of 2409