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46595 results in Computer Science

Page 1 of 2330

Mathematical Methods in Data Science
  • Bridging Theory and Applications with Python
  • Sébastien Roch
  • Coming soon
  • Expected online publication date:
    September 2025
    Print publication:
    30 September 2025
  • Bridge the gap between theoretical concepts and their practical applications with this rigorous introduction to the mathematics underpinning data science. It covers essential topics in linear algebra, calculus and optimization, and probability and statistics, demonstrating their relevance in the context of data analysis. Key application topics include clustering, regression, classification, dimensionality reduction, network analysis, and neural networks. What sets this text apart is its focus on hands-on learning. Each chapter combines mathematical insights with practical examples, using Python to implement algorithms and solve problems. Self-assessment quizzes, warm-up exercises and theoretical problems foster both mathematical understanding and computational skills. Designed for advanced undergraduate students and beginning graduate students, this textbook serves as both an invitation to data science for mathematics majors and as a deeper excursion into mathematics for data science students.

Syntax and Semantics of Petri Nets
  • Roberto Gorrieri
  • Coming soon
  • Expected online publication date:
    August 2025
    Print publication:
    31 August 2025
  • Petri nets are one of the most popular tools for modeling distributed systems. This book provides a modern look at the theory behind them, by studying three classes of nets that model (i) sequential systems, (ii) non-communicating parallel systems, and (iii) communicating parallel systems. A decidable and causality respecting behavioral equivalence is presented for each class, followed by a modal logic characterization for each equivalence. The author then introduces a suitable process algebra for the corresponding class of nets and proves that the behavioral equivalence proposed for each class is a congruence for the operator of the corresponding process algebra. Finally, an axiomatization of the behavioral congruence is proposed. The theory is introduced step by step, with ordinary-language explanations and examples provided throughout, to remain accessible to readers without specialized training in concurrency theory or formal logic. Exercises with solutions solidify understanding, and the final chapter hints at extensions of the theory.

Wireless Communications and Machine Learning

Being Human in the Digital World
  • Interdisciplinary Perspectives
  • Edited by Beate Roessler, Valerie Steeves
  • Coming soon
  • Expected online publication date:
    August 2025
    Print publication:
    31 July 2025
  • Being Human in the Digital World is a collection of essays by prominent scholars from various disciplines exploring the impact of digitization on culture, politics, health, work, and relationships. The volume raises important questions about the future of human existence in a world where machine readability and algorithmic prediction are increasingly prevalent and offers new conceptual frameworks and vocabularies to help readers understand and challenge emerging paradigms of what it means to be human. Being Human in the Digital World is an invaluable resource for readers interested in the cultural, economic, political, philosophical, and social conditions that are necessary for a good digital life. This title is also available as Open Access on Cambridge Core.

Lost in Automatic Translation
  • Navigating Life in English in the Age of Language Technologies
  • Vered Shwartz
  • Coming soon
  • Expected online publication date:
    July 2025
    Print publication:
    31 July 2025
  • The last decade has seen an exponential increase in the development and adoption of language technologies, from personal assistants such as Siri and Alexa, through automatic translation, to chatbots like ChatGPT. Yet questions remain about what we stand to lose or gain when we rely on them in our everyday lives. As a non-native English speaker living in an English-speaking country, Vered Shwartz has experienced both amusing and frustrating moments using language technologies: from relying on inaccurate automatic translation, to failing to activate personal assistants with her foreign accent. English is the world's foremost go-to language for communication, and mastering it past the point of literal translation requires acquiring not only vocabulary and grammar rules, but also figurative language, cultural references, and nonverbal communication. Will language technologies aid us in the quest to master foreign languages and better understand one another, or will they make language learning obsolete?

Introduction to Machine Learning
  • From Math to Code
  • Ruye Wang
  • Coming soon
  • Expected online publication date:
    July 2025
    Print publication:
    31 July 2025
  • Emphasizing how and why machine learning algorithms work, this introductory textbook bridges the gap between the theoretical foundations of machine learning and its practical algorithmic and code-level implementation. Over 85 thorough worked examples, in both Matlab and Python, demonstrate how algorithms are implemented and applied whilst illustrating the end result. Over 75 end-of-chapter problems empower students to develop their own code to implement these algorithms, equipping them with hands-on experience. Matlab coding examples demonstrate how a mathematical idea is converted from equations to code, and provide a jumping off point for students, supported by in-depth coverage of essential mathematics including multivariable calculus, linear algebra, probability and statistics, numerical methods, and optimization. Accompanied online by instructor lecture slides, downloadable Python code and additional appendices, this is an excellent introduction to machine learning for senior undergraduate and graduate students in Engineering and Computer Science.

Probability and Statistics for Data Science
  • Carlos Fernandez-Granda
  • Coming soon
  • Expected online publication date:
    July 2025
    Print publication:
    31 July 2025
  • This self-contained guide introduces two pillars of data science, probability theory and statistics, side by side, illuminating the connections between probabilistic concepts and the statistical techniques they employ, such as the relationship between nonparametric and parametric models and random variables. Other topics covered include hypothesis testing, principal component analysis, correlation, and regression. Examples throughout the book draw from real-world datasets, quickly demonstrating concepts in practice and confronting readers with fundamental challenges in data science, such as overfitting, the curse of dimensionality, and causal inference. Code in Python reproducing these examples is available on the book's website, along with videos, slides, and solutions to exercises. This accessible book is ideal for undergraduate and graduate students, data science practitioners, and others interested in the theoretical concepts underlying data science methods.

Paradata
  • Documenting Data Creation, Curation and Use
  • Isto Huvila, Lisa Andersson, Zanna Friberg, Ying-Hsang Liu, Olle Sköld
  • Coming soon
  • Expected online publication date:
    July 2025
    Print publication:
    31 July 2025
  • To make sense of data and use it effectively, it is essential to know where it comes from and how it has been processed and used. This is the domain of paradata, an emerging interdisciplinary field with wide applications. As digital data rapidly accumulates in repositories worldwide, this comprehensive introductory book, the first of its kind, shows how to make that data accessible and reusable. In addition to covering basic concepts of paradata, the book supports practice with coverage of methods for generating, documenting, identifying and managing paradata, including formal metadata, narrative descriptions and qualitative and quantitative backtracking. The book also develops a unifying reference model to help readers contextualise the role of paradata within a wider system of knowledge, practices and processes, and provides a vision for the future of the field. This guide to general principles and practice is ideal for researchers, students and data managers.

Scalable Monte Carlo for Bayesian Learning
  • Paul Fearnhead, Christopher Nemeth, Chris J. Oates, Chris Sherlock
  • Coming soon
  • Expected online publication date:
    July 2025
    Print publication:
    31 July 2025
  • A graduate-level introduction to advanced topics in Markov chain Monte Carlo (MCMC), as applied broadly in the Bayesian computational context. The topics covered have emerged as recently as the last decade and include stochastic gradient MCMC, non-reversible MCMC, continuous time MCMC, and new techniques for convergence assessment. A particular focus is on cutting-edge methods that are scalable with respect to either the amount of data, or the data dimension, motivated by the emerging high-priority application areas in machine learning and AI. Examples are woven throughout the text to demonstrate how scalable Bayesian learning methods can be implemented. This text could form the basis for a course and is sure to be an invaluable resource for researchers in the field.

The Cambridge Handbook of Generative AI and the Law
  • Edited by Mimi Zou, Cristina Poncibò, Martin Ebers, Ryan Calo
  • Coming soon
  • Expected online publication date:
    July 2025
    Print publication:
    31 July 2025
  • This handbook offers an important exploration of generative AI and its legal and regulatory implications from interdisciplinary perspectives. The volume is divided into four parts. Part I provides the necessary context and background to understand the topic, including its technical underpinnings and societal impacts. Part II probes the emerging regulatory and policy frameworks related to generative AI and AI more broadly across different jurisdictions. Part III analyses generative AI's impact on specific areas of law, from non-discrimination and data protection to intellectual property, corporate governance, criminal law and more. Part IV examines the various practical applications of generative AI in the legal sector and public administration. Overall, this volume provides a comprehensive resource for those seeking to understand and navigate the substantial and growing implications of generative AI for the law.

Metacognitive Artificial Intelligence
  • Edited by Paulo Shakarian, Hua Wei
  • Coming soon
  • Expected online publication date:
    July 2025
    Print publication:
    31 July 2025
  • This groundbreaking volume is designed to meet the burgeoning needs of the research community and industry. This book delves into the critical aspects of AI's self-assessment and decision-making processes, addressing the imperative for safe and reliable AI systems in high-stakes domains such as autonomous driving, aerospace, manufacturing, and military applications. Featuring contributions from leading experts, the book provides comprehensive insights into the integration of metacognition within AI architectures, bridging symbolic reasoning with neural networks, and evaluating learning agents' competency. Key chapters explore assured machine learning, handling AI failures through metacognitive strategies, and practical applications across various sectors. Covering theoretical foundations and numerous practical examples, this volume serves as an invaluable resource for researchers, educators, and industry professionals interested in fostering transparency and enhancing reliability of AI systems.

Rethinking the Lawyers' Monopoly
  • Access to Justice and the Future of Legal Services
  • Edited by David Freeman Engstrom, Nora Freeman Engstrom
  • Coming soon
  • Expected online publication date:
    July 2025
    Print publication:
    31 July 2025
  • For decades, American lawyers have enjoyed a monopoly over legal services, built upon strict unauthorized practice of law rules and prohibitions on nonlawyer ownership of law firms. Now, though, this monopoly is under threat-challenged by the one-two punch of new AI-driven technologies and a staggering access-to-justice crisis, which sees most Americans priced out of the market for legal services. At this pivotal moment, this volume brings together leading legal scholars and practitioners to propose new conceptual frameworks for reform, drawing lessons from other professions, industries, and places, both within the United States and across the world. With critical insights and thoughtful assessments, Rethinking the Lawyers' Monopoly seeks to help shape and steer the coming revolution in the legal services marketplace. This title is also available as open access on Cambridge Core.

Quantum Information
  • A First Course
  • Asma Al-Qasimi, Daniel F. V. James
  • Coming soon
  • Expected online publication date:
    June 2025
    Print publication:
    30 June 2025
  • Important concepts from the diverse fields of physics, mathematics, engineering and computer science coalesce in this foundational text on the cutting-edge field of quantum information. Designed for undergraduate and graduate students with any STEM background, and written by a highly experienced author team, this textbook draws on quantum mechanics, number theory, computer science technologies, and more, to delve deeply into learning about qubits, the building blocks of quantum information, and how they are used in quantum computing and quantum algorithms. The pedagogical structure of the chapters features exercises after each section as well as focus boxes, giving students the benefit of additional background and applications without losing sight of the big picture. Recommended further reading and answers to select exercises further support learning. Written in approachable and conversational prose, this text offers a comprehensive treatment of the exciting field of quantum information while remaining accessible to students and researchers within all STEM disciplines.

Tensor Decompositions for Data Science
  • Grey Ballard, Tamara G. Kolda
  • Coming soon
  • Expected online publication date:
    June 2025
    Print publication:
    30 June 2025
  • Tensors are essential in modern day computational and data sciences. This book explores the foundations of tensor decompositions, a data analysis methodology that is ubiquitous in machine learning, signal processing, chemometrics, neuroscience, quantum computing, financial analysis, social science, business market analysis, image processing, and much more. In this self-contained mathematical, algorithmic, and computational treatment of tensor decomposition, the book emphasizes examples using real-world downloadable open-source datasets to ground the abstract concepts. Methodologies for 3-way tensors (the simplest notation) are presented before generalizing to d-way tensors (the most general but complex notation), making the book accessible to advanced undergraduate and graduate students in mathematics, computer science, statistics, engineering, and physical and life sciences. Additionally, extensive background materials in linear algebra, optimization, probability, and statistics are included as appendices.

AI versus IP
  • Rewriting Creativity
  • Robin Feldman
  • Coming soon
  • Expected online publication date:
    June 2025
    Print publication:
    30 June 2025
  • As artificial intelligence continues to advance, it poses a threat to the very foundations of intellectual property. In AI versus IP, Robin Feldman offers a balanced perspective on the challenges we face at the intersections of AI and IP. The book examines how the advancement of AI threatens to undermine what we choose to protect with intellectual property, such as patents, trademarks, copyrights, and trade secrets, and how it derives its value. Using analogies such as the value of diamonds and the myths that support intangible rights, the book proposes potential solutions to ensure a peaceful co-existence between AI and IP. AI and IP can co-exist, Feldman argues, but only with effort and forethought.

Acting, Planning, and Learning
  • Malik Ghallab, Dana Nau, Paolo Traverso
  • Foreword by Michela Milano
  • Coming soon
  • Expected online publication date:
    June 2025
    Print publication:
    30 June 2025
  • AI's next big challenge is to master the cognitive abilities needed by intelligent agents that perform actions. Such agents may be physical devices such as robots, or they may act in simulated or virtual environments through graphic animation or electronic web transactions. This book is about integrating and automating these essential cognitive abilities: planning what actions to undertake and under what conditions, acting (choosing what steps to execute, deciding how and when to execute them, monitoring their execution, and reacting to events), and learning about ways to act and plan. This comprehensive, coherent synthesis covers a range of state-of-the-art approaches and models –deterministic, probabilistic (including MDP and reinforcement learning), hierarchical, nondeterministic, temporal, spatial, and LLMs –and applications in robotics. The insights it provides into important techniques and research challenges will make it invaluable to researchers and practitioners in AI, robotics, cognitive science, and autonomous and interactive systems.

Proof Complexity Generators
  • Jan Krajíček
  • Coming soon
  • Expected online publication date:
    May 2025
    Print publication:
    31 May 2025
  • The P vs. NP problem is one of the fundamental problems of mathematics. It asks whether propositional tautologies can be recognized by a polynomial-time algorithm. The problem would be solved in the negative if one could show that there are propositional tautologies that are very hard to prove, no matter how powerful the proof system you use. This is the foundational problem (the NP vs. coNP problem) of proof complexity, an area linking mathematical logic and computational complexity theory. Written by a leading expert in the field, this book presents a theory for constructing such hard tautologies. It introduces the theory step by step, starting with the historic background and a motivational problem in bounded arithmetic, before taking the reader on a tour of various vistas of the field. Finally, it formulates several research problems to highlight new avenues of research.

Automated Agencies
  • The Transformation of Government Guidance
  • Joshua D. Blank, Leigh Osofsky
  • Coming soon
  • Expected online publication date:
    May 2025
    Print publication:
    31 May 2025
  • Automated Agencies is the definitive account of how automation is transforming government explanations of the law to the public. Joshua D. Blank and Leigh Osofsky draw on extensive research regarding the federal government's turn to automated legal guidance through chatbots, virtual assistants, and other online tools. Blank and Osofsky argue that automated tools offer administrative benefits for both the government and the public in terms of efficiency and ease of use, yet these automated tools may also mislead members of the public. Government agencies often exacerbate this problem by making guidance seem more personalized than it is, not recognizing how users may rely on the guidance, and not disclosing that the guidance cannot be relied upon as a legal matter. After analyzing the potential costs and benefits of the use of automated legal guidance by government agencies, Automated Agencies charts a path forward for policymakers by offering detailed policy recommendations.

Quantum Resource Theories
  • Gilad Gour
  • Coming soon
  • Expected online publication date:
    May 2025
    Print publication:
    31 May 2025
  • In this original and modern book, the complexities of quantum phenomena and quantum resource theories are meticulously unravelled, from foundational entanglement and thermodynamics to the nuanced realms of asymmetry and beyond. Ideal for those aspiring to grasp the full scope of quantum resources, the text integrates advanced mathematical methods and physical principles within a comprehensive, accessible framework. Including over 760 exercises throughout, to develop and expand key concepts, readers will gain an unrivalled understanding of the topic. With its unique blend of pedagogical depth and cutting-edge research, it not only paves the way for a deep understanding of quantum resource theories but also illuminates the path toward innovative research directions. Providing the latest developments in the field as well as established knowledge within a unified framework, this book will be indispensable to students, educators, and researchers interested in quantum science's profound mysteries and applications.

Deep Learning Recommender Systems
  • Zhe Wang, Chao Pu, Felice Wang
  • Coming soon
  • Expected online publication date:
    May 2025
    Print publication:
    31 May 2025
  • Recommender systems are ubiquitous in modern life and are one of the main monetization channels for Internet technology giants. This book helps graduate students, researchers and practitioners to get to grips with this cutting-edge field and build the thorough understanding and practical skills needed to progress in the area. It not only introduces the applications of deep learning and generative AI for recommendation models, but also focuses on the industry architecture of the recommender systems. The authors include a detailed discussion of the implementation solutions used by companies such as YouTube, Alibaba, Airbnb and Netflix, as well as the related machine learning framework including model serving, model training, feature storage and data stream processing.

Page 1 of 2330