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Elsevier

Morgan Kaufmann

  • Introduction to Statistical Machine Learning

    • 2nd Edition
    • Masashi Sugiyama + 1 more
    • English
    Introduction to Statistical Machine Learning, Second Edition provides a general introduction to the fundamental concepts of statistics and probability that are used in describing machine learning algorithms, covering the two major approaches of machine learning techniques, generative methods and discriminative methods. The algorithms developed in the book include Python program code to provide readers with the necessary, practical skills needed to accomplish a wide range of data analysis tasks. The new edition also includes an all-new section on Deep Learning.

  • Modeling Systems with SysML v2

    The Systems Modeling Language
    • 1st Edition
    • Sanford Friedenthal + 2 more
    • English
    Whether you have previous experience modeling with SysML v1 or are new to modeling with SysML, Modeling Systems with SysML v2: The Systems Modeling Language provides the understanding and guidance to help you gain a basic level of competency in SysML v2, prepare for SysML v2 certification, and advance your modeling skills over time.SysML v2 is the next generation systems modeling language that can be used to model complex systems that span a wide range of application domains. It provides the ability to specify requirements, behavior, structure, analysis, and verification of interacting systems and their components while maintaining traceability across the system model. SysML v2 enhances the precision, expressiveness, consistency, usability, extensibility and interoperability of systems modeling. It provides both graphical and textual notations that facilitate stakeholder understanding and enable modern practices with agile workflows. SysML v2 is also supported by a standardized application programming interface (API) that facilitates interoperability across digital engineering tools and models. The formal semantic and syntactic foundation for SysML v2 enables technologies such as artificial intelligence (AI) with large language models (LLM) to interact with the SysML model. The combination of SysML v2's textual notation and standard API, along with a formal foundation and consistent usage patterns, facilitate automation to support model validation, workflow enhancements, and integration with other models and tools.This book provides a comprehensive and practical guide for modeling systems with SysML v2. With their unique perspective as leading contributors to the language, the authors provide a full description of the language along with examples that help you understand how to apply the language concepts. The book begins with an introduction to the most commonly used features to help you get started quickly followed by a detailed description that provides you with a more in-depth understanding. The book also includes a detailed example of applying model-based systems engineering with SysML v2 to specify and design an unmanned aerial system.

  • A Comprehensive Guide to R Programming for Data Analytics

    • 1st Edition
    • Parul Acharya
    • English
    A Comprehensive Guide to R Programming for Data Analytics provides a comprehensive presentation of univariate and multivariate statistical models within the general linear model and generalized linear model framework to analyze simple and complex data using R software. This book presents popular R packages that are used in data mining (e.g., caret-classification and regression, lubridate-dates and times, string-R for string data) and visualization (e.g., ggplot, ggthemes, ggtext). The R packages used to analyze data using a particular statistical model are explained through real-world and publicly available datasets. R codes are presented in a manner that helps readers understand the program code syntax.Examples of real-world data sets from a variety of academic disciplines are provided so that a wide audience can learn R programming to analyze data in their research. The book provides tips, recommendations, and strategies to troubleshoot common issues in R syntax, as well as definitions of key terms. Checkpoints are included to recap the concepts learned in each chapter. The book helps readers enhance their conceptual understanding and practical application of statistical models to real-world datasets, and enables readers to gain competency in R programming, which is an important skill in today’s data-driven market.

  • Artificial Intelligence and Machine Learning for Safety-Critical Systems

    A Comprehensive Guide
    • 1st Edition
    • Rajiv Pandey + 3 more
    • English
    Artificial Intelligence and Machine Learning for Safety-Critical Systems: A Comprehensive Guide provides engineers and system designers who are exploring the application of AI/ML methods for safety-critical systems with a dedicated resource on the challenges and mitigation strategies involved in their design. The book's authors present ML techniques in safety-critical systems across multiple domains, including pattern recognition, image processing, edge computing, Internet of Things (IoT), encryption, hardware accelerators, and many others. These applications help readers understand the many challenges that need to be addressed in order to increase the deployment of ML models in critical systems. In addition, the book shows how to improve public trust in ML systems by providing explainable model outputs rather than treating the system as a black box for which the outputs are difficult to explain. Finally, the authors demonstrate how to meet legal certification and regulatory requirements for the appropriate ML models. In essence, the goal of this book is to help ensure that AI-based critical systems better utilize resources, avoid failures, and increase system safety and public safety.

  • Design Research Through Practice

    From the Lab, Field, and Showroom
    • 2nd Edition
    • Ilpo Koskinen + 4 more
    • English
    Design Research Through Practice: From the Lab, Field, and Showroom is a revision of the 2011 classic design book. Written by world-renowned experts in the field of design and human-computer interaction (HCI), it focuses on one type of contemporary design research known as constructive design research. It looks at three approaches to constructive design research: laboratory research, field research, and the Showroom. The book shows how theory, research practice, and the social environment create commonalities between these approaches. It illustrates how one can successfully integrate design and research based on work carried out within industrial design and interaction design. Thoroughly revised since the original edition with contemporary case studies, up-to-date modelling, and a brand new chapter on 'Studio: research from the heart of design', the book begins with an overview of the rise of constructive design research, as well as constructive research programs and methodologies. It then describes the logic of studying design in the laboratory, design ethnography and field work, and the origins of the Showroom and its foundation in art and design rather than in science or the social sciences. It also discusses the theoretical background of constructive design research, along with modelling and prototyping of design items. Finally, it considers recent work in Lab that focuses on action and the body instead of thinking and knowing.Design Research Through Practice: From the Lab, Field, and Showroom expands on the original edition to embrace the changes to design research and design-oriented HCI, including: the rise of social design; the rise of GenAI; changes in service design and design thinking; collaborations with scientists; and the rise of craft research and so called practice-based research.

  • Human Intelligence and Artificial Intelligence

    Connecting Cognitive Science with AI
    • 1st Edition
    • Tilottama Goswami + 2 more
    • English
    Human Intelligence and Artificial Intelligence: Connecting Cognitive Science with AI offers a comprehensive, multi-dimensional view of where AI stands today in relation to human intelligence and the opportunities and challenges that lie ahead. While currently cognitive science and AI courses are offered distinctly, this book equips readers with a foundational understanding of human cognition and how it shapes the field of AI. The structure of the book is designed to move progressively from accomplished AI domains to emerging frontiers. Real-world examples, state-of-the-art models, and active research directions are paired with philosophical, psychological, and social insights to ground the technical discussions in human relevance. As human–machine interactions enter a new era with the advent of agentic AI, understanding human cognitive processes becomes essential to designing, operating, and governing agentic systems and to building a safe, trustworthy, and responsible AI ecosystem.

  • The Deterministic Universe

    Exploring Chaos, Free Will, Prediction, and Modeling
    • 1st Edition
    • Paul A. Gagniuc
    • English
    The Deterministic Universe: Exploring Chaos, Free Will, Prediction, and Modeling equips readers with the tools to learn the foundational concepts of chaos, randomness, and determinism through examples and applied case studies. The book helps readers gain insights into how deterministic algorithms handle complex, chaotic data, providing an interdisciplinary exploration of chaos theory, determinism, and free will grounded in scientific principles, computational models, and philosophical insights. The content builds on established theories in physics, bioinformatics, and systems biology, weaving them into broader existential questions. The material emphasizes the interplay between randomness, noise, and order, providing a fresh lens to view the universe.The book connects these ideas to practical tools like random number generators and nonlinear equations, machine learning algorithms, computational and predictive models, extending their implications to biological systems, human thought, and decision-making. By addressing both scientific fundamentals and philosophical debates, it bridges abstract ideas with real-world phenomena and demonstrates the role of randomness and noise in predictive models and simulations, thus helping readers understand the limits of computational systems in mimicking real-world processes.

  • Metaheuristic Optimization for Social Good

    • 1st Edition
    • Thompson Stephan + 2 more
    • English
    Metaheuristic Optimization for Social Good presents a comprehensive guide that shows how powerful optimization algorithms—genetic algorithms, swarm intelligence, evolutionary strategies, and other heuristics—can be tailored to address critical societal problems. Whereas existing references tend to focus on either the technical underpinnings of metaheuristics or broad “AI for social impact” overviews, this book demonstrates how metaheuristic approaches can be systematically and ethically applied to domains such as healthcare, urban planning, environmental stewardship, and policy-making. This handbook directly addresses these challenges by providing a structured approach to metaheuristics, presenting rigorous theoretical foundations, state-of-the-art hybrid techniques, and diverse real-world applications. The book consolidates research insights, methodological innovations, and practical use cases into a single, accessible volume. The book presents theoretical advances, hybrid model architectures, and practical applications, providing comprehensive coverage that combines metaheuristics with machine learning and data-driven simulation to enhance decision making for social good. The authors provide real-world case studies that cover the intersection of advanced optimization algorithms and multiple social domains in one volume, with technical depth in each topic. Readers learn how to ensure optimization results align with values such as fairness, equity, and sustainability, preparing readers to not only build effective solutions, but also ones that are socially acceptable and beneficial.

  • Analytical and Computational Modeling of Complex Systems

    A Systematic Approach to Understanding Real-World Complexity
    • 1st Edition
    • Florentin Paladi + 3 more
    • English
    Analytical and Computational Modeling of Complex Systems: A Systematic Approach to Understanding Real-World Complexity presents modern studies of complex systems by methods of statistical physics, numerical calculation, and computer modeling. Using general methods of investigating the branching (bifurcations) of solutions for nonlinear equations, the authors present an exhaustive analysis of the order parameter dependences on the control parameter in a small vicinity of the equilibrium values of parameters, including the stability analysis of the equilibrium states, and the asymptotic behavior of the order parameter dependences on the control parameter (bifurcation diagrams). The authors also present concepts and techniques used to model heterogeneous multi-agent interactions, complex neural networks, biochemical reaction networks in cells, and the dynamic behavior of economic systems over time. The book serves as both a learning resource and a practical reference for those engaged in the study of complex systems and their diverse real-world implementations. The book also offers a systematic learning path from theory to applications. It provides case studies that bridge the gap between mathematical formalism and complex systems modeling and simulation. Emphasizing hybrid and probabilistic modeling techniques, the book ensures greater flexibility for real-world applications. Covering a broad range of topics, it supports interdisciplinary use across departments such as physics, computer science, engineering, economics, and more.

  • Advanced Methods in Mathematics and Data Science

    Concepts and Applications
    • 1st Edition
    • Mehmet Yavuz + 2 more
    • English
    Advanced Methods in Mathematics and Data Science: Concepts and Applications provides a comprehensive guide to topics in which mathematical modelling and applications play a pivotal role, including computational models in physics and chemistry, statistical models in life science, data analysis in engineering, as well as finance and social science applications. The book provides a clear and step-by-step presentation of advanced mathematical methods at the intersection of machine learning, artificial intelligence, big data analytics, and statistics, including control theory, topology, and nonlinear dynamic systems. The book progresses from concepts and foundational methods in the early chapters, to inter-disciplinary applications in computational mathematics, mathematical biology, fractional calculus, nonlinear dynamical systems, and data-driven optimization. The methods used to analyze data using a particular computational model are thoroughly explained through real-world and publicly available data sets. Examples of real-world data sets from a variety of academic disciplines are provided so that a wide audience can learn to analyze data in their research. The book provides tips, recommendations, and strategies to troubleshoot common issues, as well as definitions of key terms. The book helps readers enhance their conceptual understanding and practical application of computational methods to real-world data sets, and enables readers to gain competency at the intersection of advanced mathematics and AI, which is an important skill in today’s data-driven market.

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