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Model-Based Machine Learning - John Winn - Bog - Taylor & Francis Inc - Plusbog.dk

Today, machine learning is being applied to a growing variety of problems in a bewildering variety of domains. A fundamental challenge when using machine learning is connecting the abstract mathematics of a machine learning technique to a concrete, real world problem. This book tackles this challenge through model-based machine learning which focuses on understanding the assumptions encoded in a machine learning system and their corresponding impact on the behaviour of the system. The key ideas of model-based machine learning are introduced through a series of case studies involving real-world applications. Case studies play a central role because it is only in the context of applications that it makes sense to discuss modelling assumptions. Each chapter introduces one case study and works through step-by-step to solve it using a model-based approach. The aim is not just to explain machine learning methods, but also showcase how to create, debug, and evolve them to solve a problem. Features: - Explores the assumptions being made by machine learning systems and the effect these assumptions have when the system is applied to concrete problems. - Explains machine learning concepts as they arise in real-world case studies. - Shows how to diagnose, understand and address problems with machine learning systems. - Full source code available, allowing models and results to be reproduced and explored. - Includes optional deep-dive sections with more mathematical details on inference algorithms for the interested reader.

Cost-Sensitive Machine Learning - - Bog - Taylor & Francis Inc - Plusbog.dk

In machine learning applications, practitioners must take into account the cost associated with the algorithm. These costs include: - Cost of acquiring training data Cost of data annotation/labeling and cleaning Computational cost for model fitting, validation, and testing Cost of collecting features/attributes for test data Cost of user feedback collection Cost of incorrect prediction/classification Cost-Sensitive Machine Learning is one of the first books to provide an overview of the current research efforts and problems in this area. It discusses real-world applications that incorporate the cost of learning into the modeling process. The first part of the book presents the theoretical underpinnings of cost-sensitive machine learning. It describes well-established machine learning approaches for reducing data acquisition costs during training as well as approaches for reducing costs when systems must make predictions for new samples. The second part covers real-world applications that effectively trade off different types of costs. These applications not only use traditional machine learning approaches, but they also incorporate cutting-edge research that advances beyond the constraining assumptions by analyzing the application needs from first principles. Spurring further research on several open problems, this volume highlights the often implicit assumptions in machine learning techniques that were not fully understood in the past. The book also illustrates the commercial importance of cost-sensitive machine learning through its coverage of the rapid application developments made by leading companies and academic research labs.

Machine Translation - Pushpak Bhattacharyya - Bog - Taylor & Francis Inc - Plusbog.dk

Three paradigms have dominated machine translation (MT)—rule-based machine translation (RBMT), statistical machine translation (SMT), and example-based machine translation (EBMT). These paradigms differ in the way they handle the three fundamental processes in MT—analysis, transfer, and generation (ATG). In its pure form, RBMT uses rules, while SMT uses data. EBMT tries a combination—data supplies translation parts that rules recombine to produce translation. Machine Translation compares and contrasts the salient principles and practices of RBMT, SMT, and EBMT. Offering an exposition of language phenomena followed by modeling and experimentation, the text: - Introduces MT against the backdrop of language divergence and the Vauquois triangle - Presents expectation maximization (EM)-based word alignment as a turning point in the history of MT - Discusses the most important element of SMT—bilingual word alignment from pairs of parallel translations - Explores the IBM models of MT, explaining how to find the best alignment given a translation pair and how to find the best translation given a new input sentence - Covers the mathematics of phrase-based SMT, phrase-based decoding, and the Moses SMT environment - Provides complete walk-throughs of the working of interlingua-based and transfer-based RBMT - Analyzes EBMT, showing how translation parts can be extracted and recombined to translate a new input, all automatically - Includes numerous examples that illustrate universal translation phenomena through the usage of specific languages Machine Translation is designed for advanced undergraduate-level and graduate-level courses in machine translation and natural language processing. The book also makes a handy professional reference for computer engineers. Print Versions of this book also include access to the ebook version.

A Guide for Machine Vision in Quality Control - L. Priya - Bog - Taylor & Francis Inc - Plusbog.dk

Machine Vision systems combine image processing with industrial automation. One of the primary areas of application of Machine Vision in the Industry is in the area of Quality Control. Machine vision provides fast, economic and reliable inspection that improves quality as well as business productivity. Building machine vision applications is a challenging task as each application is unique, with its own requirements and desired outcome. A Guide to Machine Vision in Quality Control follows a practitioner’s approach to learning machine vision. The book provides guidance on how to build machine vision systems for quality inspections. Practical applications from the Industry have been discussed to provide a good understanding of usage of machine vision for quality control. Real-world case studies have been used to explain the process of building machine vision solutions. The book offers comprehensive coverage of the essential topics, that includes: - - Introduction to Machine Vision - - - Fundamentals of Digital Images - - - Discussion of various machine vision system components - - - Digital image processing related to quality control - - - Overview of automation - The book can be used by students and academics, as well as by industry professionals, to understand the fundamentals of machine vision. Updates to the on-going technological innovations have been provided with a discussion on emerging trends in machine vision and smart factories of the future. Sheila Anand, a Doctorate in Computer Science, is working as Professor in the Department of Informaton Technology at Rajalakshmi Engineering College, Chennai, India. She has over three decades of experience in teaching, consultancy, and research. She has worked in the software industry and has extensive experience in development of software applications and in systems audit of financial, manufacturing, and trading organizations. She guides PhD aspirants and many of her research scholars have since been awarded their doctoral degree. She has published many papers in national and international journals and is a reviewer for several journals of repute. L. Priya is a PhD graduate working as Professor and Head, Department of Information Technology at Rajalakshmi Engineering College, Chennai, India. She has nearly two decades of teaching experience and good exposure to consultancy and research. She has delivered many invited talks, presented papers, and won several paper awards at international conferences. She has published several papers in international journals and is a reviewer for SCI indexed journals. Her areas of interest include machine vision, wireless communication, and machine learning.

The Resilience Machine - - Bog - Taylor & Francis Inc - Plusbog.dk

We live in a time where environmental pressures, social inequities and political derision are the backdrop of everyday life, and where resilience has become a routine prescription for coping with the conditions of modern existence. Drawing an analogy to Harvey Molotch’s urban growth machine, this book explores different narratives of resilience and their policy and practice manifestations for cities, citizens and communities. It expands on the metaphor of the machine to show how resilience can be better understood as an assemblage. Bringing together authors from multiple disciplines and different parts of the world, the book unmasks the often invisible effects of resilience strategies by examining ways in which neoliberal mentalities are fed through the rhetoric of resilience practices, policies and development projects. The contributing essays provide provocative accounts of several areas of inquiry, including biopolitics and smart bodies, resilient cities and communities, urban planning and disaster management, justice and vulnerability, and resistance to resilience. Holding out hope for critical potentials in ‘resilience,’ The Resilience Machine proposes to move beyond mechanisms of adaptation and into imagining what resilient life could look like in a more just, equitable and democratic world. The Resilience Machine is a current, vital addition to resilience, community and urban scholarship.

Computational Trust Models and Machine Learning - - Bog - Taylor & Francis Inc - Plusbog.dk

Computational Trust Models and Machine Learning provides a detailed introduction to the concept of trust and its application in various computer science areas, including multi-agent systems, online social networks, and communication systems. Identifying trust modeling challenges that cannot be addressed by traditional approaches, this book: - Explains how reputation-based systems are used to determine trust in diverse online communities - Describes how machine learning techniques are employed to build robust reputation systems - Explores two distinctive approaches to determining credibility of resources—one where the human role is implicit, and one that leverages human input explicitly - Shows how decision support can be facilitated by computational trust models - Discusses collaborative filtering-based trust aware recommendation systems - Defines a framework for translating a trust modeling problem into a learning problem - Investigates the objectivity of human feedback, emphasizing the need to filter out outlying opinions Computational Trust Models and Machine Learning effectively demonstrates how novel machine learning techniques can improve the accuracy of trust assessment.

Machine Learning - Muhammad Badruddin Khan - Bog - Taylor & Francis Inc - Plusbog.dk

Machine learning, one of the top emerging sciences, has an extremely broad range of applications. However, many books on the subject provide only a theoretical approach, making it difficult for a newcomer to grasp the subject material. This book provides a more practical approach by explaining the concepts of machine learning algorithms and describing the areas of application for each algorithm, using simple practical examples to demonstrate each algorithm and showing how different issues related to these algorithms are applied.

Machine Learning - Stephen (massey University Marsland - Bog - Taylor & Francis Inc - Plusbog.dk

A Proven, Hands-On Approach for Students without a Strong Statistical Foundation Since the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students without a strong statistical background often find it hard to get started in this area. Remedying this deficiency, Machine Learning: An Algorithmic Perspective, Second Edition helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation. New to the Second Edition - Two new chapters on deep belief networks and Gaussian processes - Reorganization of the chapters to make a more natural flow of content - Revision of the support vector machine material, including a simple implementation for experiments - New material on random forests, the perceptron convergence theorem, accuracy methods, and conjugate gradient optimization for the multi-layer perceptron - Additional discussions of the Kalman and particle filters - Improved code, including better use of naming conventions in Python Suitable for both an introductory one-semester course and more advanced courses, the text strongly encourages students to practice with the code. Each chapter includes detailed examples along with further reading and problems. All of the code used to create the examples is available on the author’s website.

The Soft Machine - David Porush - Bog - Taylor & Francis Inc - Plusbog.dk

The Soft Machine , originally published in 1985, represents a significant contribution to the study of contemporary literature in the larger cultural and scientific context. David Porush shows how the concepts of cybernetics and artificial intelligence that have sparked our present revolution in computer and information technology have also become the source for images and techniques in our most highly sophisticated literature, postmodern fiction by Barthelme, Barth, Pynchon, Beckett, Burroughs, Vonnegut and others. With considerable skill, Porush traces the growth of "the metaphor of the machine" as it evolves both technologically and in literature of the twentieth century. He describes the birth of cybernetics, gives one of the clearest accounts for a lay audience of its major concepts and shows the growth of philosophical resistance to the mechanical model for human intelligence and communication which cybernetics promotes, a model that had grown increasingly influential in the previous decade. The Soft Machine shows postmodern fiction synthesizing the inviting metaphors and concepts of cybernetics with the ideals of art, a synthesis that results in what Porush calls "cybernetic fiction" alive to the myths and images of a cybernetic age.

God of the Machine - Isabel Paterson - Bog - Taylor & Francis Inc - Plusbog.dk

The God of the Machine presents an original theory of history and a bold defense of individualism as the source of moral and political progress. When it was published in 1943, Isabel Paterson''s work provided fresh intellectual support for the endangered American belief in individual rights, limited government, and economic freedom. The crisis of today''s collectivized nations would not have surprised Paterson; in The God of the Machine, she had explored the reasons for collectivism''s failure. Her book placed her in the vanguard of the free-enterprise movement now sweeping the world. Paterson sees the individual creative mind as the dynamo of history, and respect for the individual''s God-given rights as the precondition for the enormous release of energy that produced the modern world. She sees capitalist institutions as the machinery through which human energy works, and government as a device properly used merely to cut off power to activities that threaten personal liberty. Paterson applies her general theory to particular issues in contemporary life, such as education, .social welfare, and the causes of economic distress. She severely criticizes all but minimal application of government, including governmental interventions that most people have long taken for granted. The God of the Machine offers a challenging perspective on the continuing, worldwide debate about the nature of freedom, the uses of power, and the prospects of human betterment. Stephen Cox''s substantial introduction to The God of the Machine is a comprehensive and enlightening account of Paterson''s colorful life and work. He describes The God of the Machine as "not just theory, but rhapsody, satire, diatribe, poetic narrative." Paterson''s work continues to be relevant because "it exposes the moral and practical failures of collectivism, failures that are now almost universally acknowledged but are still far from universally understood." The book will be essential to students of American history, political theory, and literature.

A First Course in Machine Learning - Simon Rogers - Bog - Taylor & Francis Inc - Plusbog.dk

" A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility, starts from a detailed explanation of the basic foundations of Bayesian analysis in the simplest of settings, and goes all the way to the frontiers of the subject such as infinite mixture models, GPs, and MCMC." —Devdatt Dubhashi, Professor, Department of Computer Science and Engineering, Chalmers University, Sweden "This textbook manages to be easier to read than other comparable books in the subject while retaining all the rigorous treatment needed. The new chapters put it at the forefront of the field by covering topics that have become mainstream in machine learning over the last decade." —Daniel Barbara, George Mason University, Fairfax, Virginia, USA "The new edition of A First Course in Machine Learning by Rogers and Girolami is an excellent introduction to the use of statistical methods in machine learning. The book introduces concepts such as mathematical modeling, inference, and prediction, providing ‘just in time’ the essential background on linear algebra, calculus, and probability theory that the reader needs to understand these concepts." —Daniel Ortiz-Arroyo, Associate Professor, Aalborg University Esbjerg, Denmark "I was impressed by how closely the material aligns with the needs of an introductory course on machine learning, which is its greatest strength…Overall, this is a pragmatic and helpful book, which is well-aligned to the needs of an introductory course and one that I will be looking at for my own students in coming months." —David Clifton, University of Oxford, UK "The first edition of this book was already an excellent introductory text on machine learning for an advanced undergraduate or taught masters level course, or indeed for anybody who wants to learn about an interesting and important field of computer science. The additional chapters of advanced material on Gaussian process, MCMC and mixture modeling provide an ideal basis for practical projects, without disturbing the very clear and readable exposition of the basics contained in the first part of the book." —Gavin Cawley, Senior Lecturer, School of Computing Sciences, University of East Anglia, UK "This book could be used for junior/senior undergraduate students or first-year graduate students, as well as individuals who want to explore the field of machine learning…The book introduces not only the concepts but the underlying ideas on algorithm implementation from a critical thinking perspective." —Guangzhi Qu, Oakland University, Rochester, Michigan, USA

Soil-Machine Interactions - Jie Shen - Bog - Taylor & Francis Inc - Plusbog.dk

Aiming to improve work efficiency in such areas as tillage in agriculture, earth-moving in civil engineering, and tunnel-making in sea-bed operations, this work offers an introduction to Finite Element Method (FEM) analysis of soil-machine systems. It explains the advantage of FEM''s numerical approach over traditional analytical and empirical methods of dealing with complex factors from nonlinear mechanical behaviour to geometric configurations.

Fundamentals of Machine Elements - Bernard J. Hamrock - Bog - Taylor & Francis Inc - Plusbog.dk

New and Improved SI Edition—Uses SI Units Exclusively in the Text Adapting to the changing nature of the engineering profession, this third edition of Fundamentals of Machine Elements aggressively delves into the fundamentals and design of machine elements with an SI version. This latest edition includes a plethora of pedagogy, providing a greater understanding of theory and design. Significantly Enhanced and Fully Illustrated The material has been organized to aid students of all levels in design synthesis and analysis approaches, to provide guidance through design procedures for synthesis issues, and to expose readers to a wide variety of machine elements. Each chapter contains a quote and photograph related to the chapter as well as case studies, examples, design procedures, an abstract, list of symbols and subscripts, recommended readings, a summary of equations, and end-of-chapter problems. What’s New in the Third Edition: - Covers life cycle engineering - Provides a description of the hardness and common hardness tests - Offers an inclusion of flat groove stress concentration factors - Adds the staircase method for determining endurance limits and includes Haigh diagrams to show the effects of mean stress - Discusses typical surface finishes in machine elements and manufacturing processes used to produce them - Presents a new treatment of spline, pin, and retaining ring design, and a new section on the design of shaft couplings - Reflects the latest International Standards Organization standards - Simplifies the geometry factors for bevel gears - Includes a design synthesis approach for worm gears - Expands the discussion of fasteners and welds - Discusses the importance of the heat affected zone for weld quality - Describes the classes of welds and their analysis methods - Considers gas springs and wave springs - Contains the latest standards and manufacturer’s recommendations on belt design, chains, and wire ropes The text also expands the appendices to include a wide variety of material properties, geometry factors for fracture analysis, and new summaries of beam deflection.

The Hollywood War Machine - Tom Pollard - Bog - Taylor & Francis Inc - Plusbog.dk

The newly expanded and revised edition of The Hollywood War Machine includes wide-ranging exploration of numerous popular military-themed films that have appeared in the close to a decade since the first edition was published. Within the Hollywood movie community, there has not been even the slightest decline in well-financed pictures focusing on warfare and closely-related motifs. The second edition includes a new chapter on recent popular films and another that analyzes the relationship between these movies and the bourgeoning gun culture in the United States, marked in recent years by a dramatic increase in episodes of mass killings.

A Concise Introduction to Machine Learning - A.c. Faul - Bog - Taylor & Francis Inc - Plusbog.dk

The emphasis of the book is on the question of Why – only if why an algorithm is successful is understood, can it be properly applied, and the results trusted. Algorithms are often taught side by side without showing the similarities and differences between them. This book addresses the commonalities, and aims to give a thorough and in-depth treatment and develop intuition, while remaining concise. This useful reference should be an essential on the bookshelves of anyone employing machine learning techniques. The author''s webpage for the book can be accessed here .

Artificial Intelligence and Machine Learning in Cybersecurity - Phd Young - Bog - Taylor & Francis Inc - Plusbog.dk

"Leveraging Artificial Intelligence (AI) and Machine Learning to Improve Cybersecurity Protocols" is a comprehensive exploration of the intersection between cutting-edge technology and cybersecurity practices. This book offers readers an in-depth understanding of how AI and ML are reshaping the cybersecurity landscape. It begins with foundational concepts, explaining AI and ML''s principles and their transformative potential within various sectors, particularly cybersecurity. This book uniquely combines theoretical insights and practical applications, making it an essential resource for graduate students and cybersecurity professionals eager to expand their knowledge and skills.The book''s uniqueness lies in its detailed analysis of how AI and machine learning can predict and counteract emerging threats in real-time, shifting the paradigm from reactive to proactive cybersecurity measures. By delving into a wide range of topics, such as AI- powered Intrusion Detection and Prevention Systems (IDPS) and Endpoint Security, the author provides case studies and examples from sectors like finance and healthcare. This hands-on approach not only illustrates successful implementations but also highlights potential challenges, offering balanced perspectives and strategies to overcome hurdles. The inclusion of ethical considerations around AI usage in cybersecurity further distinguishes it as a forward-thinking guide.As cyber threats continue to evolve, the need for advanced AI and ML methodologies becomes increasingly critical. This book addresses this urgency by equipping readers with contemporary knowledge and tools necessary to leverage these technologies effectively. The discussion of future trends, such as AI-powered quantum security and necessary policy implications, ensures that readers are well-prepared to navigate the complexities of cybersecurity in the coming decades. Ultimately, it serves as both an educational textbook for students and a practical guide for cyber practitioners, offering a roadmap for implementing AI-driven cybersecurity solutions that enhance threat detection, response, and prevention.

Global Perspectives on the Ecology of Human-Machine Systems - - Bog - Taylor & Francis Inc - Plusbog.dk

There is a growing consensus in the human factors/ergonomics community that human factors research has had little impact on significant applied problems. Some have suggested that the problem lies in the fact that much HF/E research has been based on the wrong type of psychology, an information processing view of psychology that is reductionistic and context-free. Ecological psychology offers a viable alternative, presenting a richer view of human behavior that is holistic and contextualized. The papers presented in these two volumes show the conceptual impact that ecological psychology can have on HF/E, as well as presenting a number of specific examples illustrating the ecological approach to human-machine systems. It is the first collection of papers that explicitly draws a connection between these two fields. While work in this area is only just beginning, the evidence available suggests that taking an ecological approach to human factors/ergonomics helps bridge the existing gap between basic research and applied problems.

Local Applications of the Ecological Approach To Human-Machine Systems - - Bog - Taylor & Francis Inc - Plusbog.dk

There is a growing consensus in the human factors/ergonomics community that human factors research has had little impact on significant applied problems. Some have suggested that the problem lies in the fact that much HF/E research has been based on the wrong type of psychology, an information processing view of psychology that is reductionistic and context-free. Ecological psychology offers a viable alternative, presenting a richer view of human behavior that is holistic and contextualized. The papers presented in these two volumes show the conceptual impact that ecological psychology can have on HF/E, as well as presenting a number of specific examples illustrating the ecological approach to human-machine systems. It is the first collection of papers that explicitly draws a connection between these two fields. While work in this area is only just beginning, the evidence available suggests that taking an ecological approach to human factors/ergonomics helps bridge the existing gap between basic research and applied problems.

The Diversity Machine - Frederick R. Lynch - Bog - Taylor & Francis Inc - Plusbog.dk

"Diversity" has become the turn-of-the-century buzzword. Republican and Democratic leaders ritually chant "diversity is our strength" and corporate CEOs talk about the need to create a "workforce that looks like America." Most corporate mission statements now contain a clause on "valuing differences" and millions of employees have completed-or soon will undergo-some sort of "diversity training." Where did all this come from -and why? Who created diversity programs? How do they differ? How effective are these policies? Can they do more harm than good in organizations and in the wider society? During the past decade, sociologist Frederick R. Lynch studied the rise of a social policy movement that has successfully moved multiculturalism from universities and foundations into the courts, mass media, and the American workplace. The new diversity policies are future-oriented and market-driven, eclipsing "old" affirmative action debates about overcoming past discrimination against blacks.Based on more than six years of field research and hundreds of interviews, Lynch tracks the development and impact of different forms of diversity policies at dozens of consultant gatherings, in the business and professional literature and through in-depth case studies such as the Los Angeles Sheriff''s Department and the University of Michigan, Ann Arbor. He profiles the major consultants who have powered the diversity machine, analyzes the benefits and drawbacks of various approaches to workplace diversity and provides numerous "you-are-there" samples of workshops, seminars, and conferences. The book is written for the general reader interested in public-policy issues, social scientists, and others interested in the origins and consequences of workplace diversity policies.

Regularization, Optimization, Kernels, and Support Vector Machines - - Bog - Taylor & Francis Inc - Plusbog.dk

Regularization, Optimization, Kernels, and Support Vector Machines offers a snapshot of the current state of the art of large-scale machine learning, providing a single multidisciplinary source for the latest research and advances in regularization, sparsity, compressed sensing, convex and large-scale optimization, kernel methods, and support vector machines. Consisting of 21 chapters authored by leading researchers in machine learning, this comprehensive reference: - Covers the relationship between support vector machines (SVMs) and the Lasso - Discusses multi-layer SVMs - Explores nonparametric feature selection, basis pursuit methods, and robust compressive sensing - Describes graph-based regularization methods for single- and multi-task learning - Considers regularized methods for dictionary learning and portfolio selection - Addresses non-negative matrix factorization - Examines low-rank matrix and tensor-based models - Presents advanced kernel methods for batch and online machine learning, system identification, domain adaptation, and image processing - Tackles large-scale algorithms including conditional gradient methods, (non-convex) proximal techniques, and stochastic gradient descent Regularization, Optimization, Kernels, and Support Vector Machines is ideal for researchers in machine learning, pattern recognition, data mining, signal processing, statistical learning, and related areas.

Statistical Reinforcement Learning - Hirotaka Hachiya - Bog - Taylor & Francis Inc - Plusbog.dk

Reinforcement learning is a mathematical framework for developing computer agents that can learn an optimal behavior by relating generic reward signals with its past actions. With numerous successful applications in business intelligence, plant control, and gaming, the RL framework is ideal for decision making in unknown environments with large amounts of data. Supplying an up-to-date and accessible introduction to the field, Statistical Reinforcement Learning: Modern Machine Learning Approaches presents fundamental concepts and practical algorithms of statistical reinforcement learning from the modern machine learning viewpoint. It covers various types of RL approaches, including model-based and model-free approaches, policy iteration, and policy search methods. - Covers the range of reinforcement learning algorithms from a modern perspective - Lays out the associated optimization problems for each reinforcement learning scenario covered - Provides thought-provoking statistical treatment of reinforcement learning algorithms - The book covers approaches recently introduced in the data mining and machine learning fields to provide a systematic bridge between RL and data mining/machine learning researchers. It presents state-of-the-art results, including dimensionality reduction in RL and risk-sensitive RL. Numerous illustrative examples are included to help readers understand the intuition and usefulness of reinforcement learning techniques. This book is an ideal resource for graduate-level students in computer science and applied statistics programs, as well as researchers and engineers in related fields.

The Most Complex Machine - David J. Eck - Bog - Taylor & Francis Inc - Plusbog.dk

This introduction to computers presents the fundamental ideas and principles on which modern computers are built. While used as a text for courses in computer appreciation as well as introductions to computer science, the book has found a wide audience among computer users who wish to understand the basis of the machines that form and transform our society. What Computers Do • Teaching Silicon to Compute • Building a Computer •†Theoretical Computers • Real Computers • Programming • Subroutines and Recursion • Real Programming Languages • Applications • Cooperating Computers • Graphics • Artificial Intelligence • Answers • The text is supplemented by a web site that gives access to other problems and projects.

The Most Complex Machine - David J. Eck - Bog - Taylor & Francis Inc - Plusbog.dk

This introduction to computers presents the fundamental ideas and principles on which modern computers are built. While used as a text for courses in computer appreciation as well as introductions to computer science, the book has found a wide audience among computer users who wish to understand the basis of the machines that form and transform our society. What Computers Do • Teaching Silicon to Compute • Building a Computer •†Theoretical Computers • Real Computers • Programming • Subroutines and Recursion • Real Programming Languages • Applications • Cooperating Computers • Graphics • Artificial Intelligence • Answers • The text is supplemented by a web site that gives access to other problems and projects.

A Machine That Would Go of Itself - Russell Fraser - Bog - Taylor & Francis Inc - Plusbog.dk

In this volume, Pulitzer Prize-winning historian Michael Kammen explores the U.S. Constitution''s place in the public consciousness and its role as a symbol in American life, from ratification in 1788 to our own time. As he examines what the Constitution has meant to the American people (perceptions and misperceptions, uses and abuses, knowledge and ignorance), Kammen shows that although there are recurrent declarations of reverence most of us neither know nor fully understand our Constitution. How did this gap between ideal and reality come about? To explain it, Kammen examines the complex and contradictory feelings about the Constitution that emerged during its preparation and that have been with us ever since. He begins with our confusion as to the kind of Union we created, especially with regard to how much sovereignty the states actually surrendered to the central government. This confusion is the source of the constitutional crisis that led to the Civil War and its aftermath. Kammen also describes and analyzes changing perceptions of the differences and similarities between the British and American constitutions; turn-of-the-century debates about states'' rights versus national authority; and disagreements about how easy or difficult it ought to be to amend the Constitution. Moving into the twentieth century, he notes the development of a "cult of the Constitution" following World War I, and the conflict over policy issues that persisted despite a shared commitment to the Constitution.

Data Mining and Machine Learning in Cybersecurity - Xian Du - Bog - Taylor & Francis Inc - Plusbog.dk