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Quantum Machine Learning - - Bog - Taylor & Francis Ltd - Plusbog.dk

Quantum Machine Learning - - Bog - Taylor & Francis Ltd - Plusbog.dk

This book presents the research into and application of machine learning in quantum computation, known as quantum machine learning (QML). It presents a comparison of quantum machine learning, classical machine learning, and traditional programming, along with the usage of quantum computing, toward improving traditional machine learning algorithms through case studies. In summary, the book: - - Covers the core and fundamental aspects of statistics, quantum learning, and quantum machines. - - - Discusses the basics of machine learning, regression, supervised and unsupervised machine learning algorithms, and artificial neural networks. - - - Elaborates upon quantum machine learning models, quantum machine learning approaches and quantum classification, and boosting. - - - Introduces quantum evaluation models, deep quantum learning, ensembles, and QBoost. - - - Presents case studies to demonstrate the efficiency of quantum mechanics in industrial aspects. - This reference text is primarily written for scholars and researchers working in the fields of computer science and engineering, information technology, electrical engineering, and electronics and communication engineering.

DKK 901.00
1

The Human Factor in Machine Translation - - Bog - Taylor & Francis Ltd - Plusbog.dk

The Human Factor in Machine Translation - - Bog - Taylor & Francis Ltd - Plusbog.dk

Machine translation has become increasingly popular, especially with the introduction of neural machine translation in major online translation systems. However, despite the rapid advances in machine translation, the role of a human translator remains crucial. As illustrated by the chapters in this book, man-machine interaction is essential in machine translation, localisation, terminology management, and crowdsourcing translation. In fact, the importance of a human translator before, during, and after machine processing, cannot be overemphasised as human intervention is the best way to ensure the translation quality of machine translation. This volume explores the role of a human translator in machine translation from various perspectives, affording a comprehensive look at this topical research area. This book is essential reading for anyone involved in translation studies, machine translation or interested in translation technology.

DKK 468.00
1

Functional Reverse Engineering of Machine Tools - - Bog - Taylor & Francis Ltd - Plusbog.dk

Functional Reverse Engineering of Machine Tools - - Bog - Taylor & Francis Ltd - Plusbog.dk

The purpose of this book is to develop capacity building in strategic and non-strategic machine tool technology. The book contains chapters on how to functionally reverse engineer strategic and non-strategic computer numerical control machinery. Numerous engineering areas, such as mechanical engineering, electrical engineering, control engineering, and computer hardware and software engineering, are covered. The book offers guidelines and covers design for machine tools, prototyping, augmented reality for machine tools, modern communication strategies, and enterprises of functional reverse engineering, along with case studies. Features - - Presents capacity building in machine tool development - - - Discusses engineering design for machine tools - - - Covers prototyping of strategic and non-strategic machine tools - - - Illustrates augmented reality for machine tools - - - Includes Internet of Things (IoT) for machine tools -

DKK 505.00
1

Machine Learning for Healthcare - - Bog - Taylor & Francis Ltd - Plusbog.dk

Machine Learning for Healthcare - - Bog - Taylor & Francis Ltd - Plusbog.dk

Machine Learning for Healthcare: Handling and Managing Data provides in-depth information about handling and managing healthcare data through machine learning methods. This book expresses the long-standing challenges in healthcare informatics and provides rational explanations of how to deal with them. Machine Learning for Healthcare: Handling and Managing Data provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of machine learning applications. These are illustrated in a case study which examines how chronic disease is being redefined through patient-led data learning and the Internet of Things. This text offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare. Readers will discover the ethical implications of machine learning in healthcare and the future of machine learning in population and patient health optimization. This book can also help assist in the creation of a machine learning model, performance evaluation, and the operationalization of its outcomes within organizations. It may appeal to computer science/information technology professionals and researchers working in the area of machine learning, and is especially applicable to the healthcare sector. The features of this book include: - - A unique and complete focus on applications of machine learning in the healthcare sector. - - - An examination of how data analysis can be done using healthcare data and bioinformatics. - - - An investigation of how healthcare companies can leverage the tapestry of big data to discover new business values. - - - An exploration of the concepts of machine learning, along with recent research developments in healthcare sectors. -

DKK 467.00
1

Machine Learning for Healthcare - - Bog - Taylor & Francis Ltd - Plusbog.dk

Machine Learning for Healthcare - - Bog - Taylor & Francis Ltd - Plusbog.dk

Machine Learning for Healthcare: Handling and Managing Data provides in-depth information about handling and managing healthcare data through machine learning methods. This book expresses the long-standing challenges in healthcare informatics and provides rational explanations of how to deal with them. Machine Learning for Healthcare: Handling and Managing Data provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of machine learning applications. These are illustrated in a case study which examines how chronic disease is being redefined through patient-led data learning and the Internet of Things. This text offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare. Readers will discover the ethical implications of machine learning in healthcare and the future of machine learning in population and patient health optimization. This book can also help assist in the creation of a machine learning model, performance evaluation, and the operationalization of its outcomes within organizations. It may appeal to computer science/information technology professionals and researchers working in the area of machine learning, and is especially applicable to the healthcare sector. The features of this book include: - - A unique and complete focus on applications of machine learning in the healthcare sector. - - - An examination of how data analysis can be done using healthcare data and bioinformatics. - - - An investigation of how healthcare companies can leverage the tapestry of big data to discover new business values. - - - An exploration of the concepts of machine learning, along with recent research developments in healthcare sectors. -

DKK 993.00
1

Machine Intelligence - - Bog - Taylor & Francis Ltd - Plusbog.dk

Machine Intelligence - - Bog - Taylor & Francis Ltd - Plusbog.dk

Machines are being systematically empowered to be interactive and intelligent in their operations, offerings. and outputs. There are pioneering Artificial Intelligence (AI) technologies and tools. Machine and Deep Learning (ML/DL) algorithms, along with their enabling frameworks, libraries, and specialized accelerators, find particularly useful applications in computer and machine vision, human machine interfaces (HMIs), and intelligent machines. Machines that can see and perceive can bring forth deeper and decisive acceleration, automation, and augmentation capabilities to businesses as well as people in their everyday assignments. Machine vision is becoming a reality because of advancements in the computer vision and device instrumentation spaces. Machines are increasingly software-defined. That is, vision-enabling software and hardware modules are being embedded in new-generation machines to be self-, surroundings, and situation-aware. Machine Intelligence: Computer Vision and Natural Language Processing emphasizes computer vision and natural language processing as drivers of advances in machine intelligence. The book examines these technologies from the algorithmic level to the applications level. It also examines the integrative technologies enabling intelligent applications in business and industry. Features: - Motion images object detection over voice using deep learning algorithms - Ubiquitous computing and augmented reality in HCI - Learning and reasoning in Artificial Intelligence - Economic sustainability, mindfulness, and diversity in the age of artificial intelligence and machine learning - Streaming analytics for healthcare and retail domains Covering established and emerging technologies in machine vision, the book focuses on recent and novel applications and discusses state-of-the-art technologies and tools.

DKK 630.00
1

Applied Machine Learning Using mlr3 in R - - Bog - Taylor & Francis Ltd - Plusbog.dk

Applied Machine Learning Using mlr3 in R - - Bog - Taylor & Francis Ltd - Plusbog.dk

mlr3 is an award-winning ecosystem of R packages that have been developed to enable state-of-the-art machine learning capabilities in R. Applied Machine Learning Using mlr3 in R gives an overview of flexible and robust machine learning methods, with an emphasis on how to implement them using mlr3 in R. It covers various key topics, including basic machine learning tasks, such as building and evaluating a predictive model; hyperparameter tuning of machine learning approaches to obtain peak performance; building machine learning pipelines that perform complex operations such as pre-processing followed by modelling followed by aggregation of predictions; and extending the mlr3 ecosystem with custom learners, measures, or pipeline components. Features: - In-depth coverage of the mlr3 ecosystem for users and developers - Explanation and illustration of basic and advanced machine learning concepts - Ready to use code samples that can be adapted by the user for their application - Convenient and expressive machine learning pipelining enabling advanced modelling - Coverage of topics that are often ignored in other machine learning books The book is primarily aimed at researchers, practitioners, and graduate students who use machine learning or who are interested in using it. It can be used as a textbook for an introductory or advanced machine learning class that uses R, as a reference for people who work with machine learning methods, and in industry for exploratory experiments in machine learning.

DKK 656.00
1

Coverbal Synchrony in Human-Machine Interaction - - Bog - Taylor & Francis Ltd - Plusbog.dk

Coverbal Synchrony in Human-Machine Interaction - - Bog - Taylor & Francis Ltd - Plusbog.dk

Embodied conversational agents (ECA) and speech-based human–machine interfaces can together represent more advanced and more natural human–machine interaction. Fusion of both topics is a challenging agenda in research and production spheres. The important goal of human–machine interfaces is to provide content or functionality in the form of a dialog resembling face-to-face conversations. All natural interfaces strive to exploit and use different communication strategies that provide additional meaning to the content, whether they are human–machine interfaces for controlling an application or different ECA-based human–machine interfaces directly simulating face-to-face conversation. Coverbal Synchrony in Human-Machine Interaction presents state-of-the-art concepts of advanced environment-independent multimodal human–machine interfaces that can be used in different contexts, ranging from simple multimodal web-browsers (for example, multimodal content reader) to more complex multimodal human–machine interfaces for ambient intelligent environments (such as supportive environments for elderly and agent-guided household environments). They can also be used in different computing environments—from pervasive computing to desktop environments. Within these concepts, the contributors discuss several communication strategies, used to provide different aspects of human–machine interaction.

DKK 643.00
1

Machine-to-Machine Communications - - Bog - Taylor & Francis Ltd - Plusbog.dk

Machine-to-Machine Communications - - Bog - Taylor & Francis Ltd - Plusbog.dk

With the number of machine-to-machine (M2M)–enabled devices projected to reach 20 to 50 billion by 2020, there is a critical need to understand the demands imposed by such systems. Machine-to-Machine Communications: Architectures, Technology, Standards, and Applications offers rigorous treatment of the many facets of M2M communication, including its integration with current technology. Presenting the work of a different group of international experts in each chapter, the book begins by supplying an overview of M2M technology. It considers proposed standards, cutting-edge applications, architectures, and traffic modeling and includes case studies that highlight the differences between traditional and M2M communications technology. - Details a practical scheme for the forward error correction code design - Investigates the effectiveness of the IEEE 802.15.4 low data rate wireless personal area network standard for use in M2M communications - Identifies algorithms that will ensure functionality, performance, reliability, and security of M2M systems - Illustrates the relationship between M2M systems and the smart power grid - Presents techniques to ensure integration with and adaptation of existing communication systems to carry M2M traffic Providing authoritative insights into the technologies that enable M2M communications, the book discusses the challenges posed by the use of M2M communications in the smart grid from the aspect of security and proposes an efficient intrusion detection system to deal with a number of possible attacks. After reading this book, you will develop the understanding required to solve problems related to the design, deployment, and operation of M2M communications networks and systems.

DKK 505.00
1

Machine Learning for Business Analytics - - Bog - Taylor & Francis Ltd - Plusbog.dk

Machine Learning for Business Analytics - - Bog - Taylor & Francis Ltd - Plusbog.dk

Machine Learning is an integral tool in a business analyst’s arsenal because the rate at which data is being generated from different sources is increasing and working on complex unstructured data is becoming inevitable. Data collection, data cleaning, and data mining are rapidly becoming more difficult to analyze than just importing information from a primary or secondary source. The machine learning model plays a crucial role in predicting the future performance and results of a company. In real-time, data collection and data wrangling are the important steps in deploying the models. Analytics is a tool for visualizing and steering data and statistics. Business analysts can work with different datasets -- choosing an appropriate machine learning model results in accurate analyzing, forecasting the future, and making informed decisions. The global machine learning market was valued at $1.58 billion in 2017 and is expected to reach $20.83 billion in 2024 -- growing at a CAGR of 44.06% between 2017 and 2024. The authors have compiled important knowledge on machine learning real-time applications in business analytics. This book enables readers to get broad knowledge in the field of machine learning models and to carry out their future research work. The future trends of machine learning for business analytics are explained with real case studies. Essentially, this book acts as a guide to all business analysts. The authors blend the basics of data analytics and machine learning and extend its application to business analytics. This book acts as a superb introduction and covers the applications and implications of machine learning. The authors provide first-hand experience of the applications of machine learning for business analytics in the section on real-time analysis. Case studies put the theory into practice so that you may receive hands-on experience with machine learning and data analytics. This book is a valuable source for practitioners, industrialists, technologists, and researchers.

DKK 542.00
1

Machine Learning - Jugal Kalita - Bog - Taylor & Francis Ltd - Plusbog.dk

Machine Learning - Jugal Kalita - Bog - Taylor & Francis Ltd - Plusbog.dk

Machine Learning: Theory and Practice provides an introduction to the most popular methods in machine learning. The book covers regression including regularization, tree-based methods including Random Forests and Boosted Trees, Artificial Neural Networks including Convolutional Neural Networks (CNNs), reinforcement learning, and unsupervised learning focused on clustering. Topics are introduced in a conceptual manner along with necessary mathematical details. The explanations are lucid, illustrated with figures and examples. For each machine learning method discussed, the book presents appropriate libraries in the R programming language along with programming examples. Features: - - Provides an easy-to-read presentation of commonly used machine learning algorithms in a manner suitable for advanced undergraduate or beginning graduate students, and mathematically and/or programming-oriented individuals who want to learn machine learning on their own. - - - Covers mathematical details of the machine learning algorithms discussed to ensure firm understanding, enabling further exploration - - - Presents worked out suitable programming examples, thus ensuring conceptual, theoretical and practical understanding of the machine learning methods. - This book is aimed primarily at introducing essential topics in Machine Learning to advanced undergraduates and beginning graduate students. The number of topics has been kept deliberately small so that it can all be covered in a semester or a quarter. The topics are covered in depth, within limits of what can be taught in a short period of time. Thus, the book can provide foundations that will empower a student to read advanced books and research papers.

DKK 476.00
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Machine Learning for Neuroscience - Chuck Easttom - Bog - Taylor & Francis Ltd - Plusbog.dk

Machine Learning for Neuroscience - Chuck Easttom - Bog - Taylor & Francis Ltd - Plusbog.dk

This book addresses the growing need for machine learning and data mining in neuroscience. The book offers a basic overview of the neuroscience, machine learning and the required math and programming necessary to develop reliable working models. The material is presented in a easy to follow user-friendly manner and is replete with fully working machine learning code. Machine Learning for Neuroscience: A Systematic Approach, tackles the needs of neuroscience researchers and practitioners that have very little training relevant to machine learning. The first section of the book provides an overview of necessary topics in order to delve into machine learning, including basic linear algebra and Python programming. The second section provides an overview of neuroscience and is directed to the computer science oriented readers. The section covers neuroanatomy and physiology, cellular neuroscience, neurological disorders and computational neuroscience. The third section of the book then delves into how to apply machine learning and data mining to neuroscience and provides coverage of artificial neural networks (ANN), clustering, and anomaly detection. The book contains fully working code examples with downloadable working code. It also contains lab assignments and quizzes, making it appropriate for use as a textbook. The primary audience is neuroscience researchers who need to delve into machine learning, programmers assigned neuroscience related machine learning projects and students studying methods in computational neuroscience.

DKK 929.00
1

Machine Learning for Managers - Paul (university Of Auckland Geertsema - Bog - Taylor & Francis Ltd - Plusbog.dk

Machine Learning for Managers - Paul (university Of Auckland Geertsema - Bog - Taylor & Francis Ltd - Plusbog.dk

Machine learning can help managers make better predictions, automate complex tasks and improve business operations. Managers who are familiar with machine learning are better placed to navigate the increasingly digital world we live in. There is a view that machine learning is a highly technical subject that can only be understood by specialists. However, many of the ideas that underpin machine learning are straightforward and accessible to anyone with a bit of curiosity. This book is for managers who want to understand what machine learning is about, but who lack a technical background in computer science, statistics or math. The book describes in plain language what machine learning is and how it works. In addition, it explains how to manage machine learning projects within an organization. This book should appeal to anyone that wants to learn more about using machine learning to drive value in real-world organizations.

DKK 363.00
1

Practical Machine Learning - Noe E. Nnko - Bog - Taylor & Francis Ltd - Plusbog.dk

Practical Machine Learning - Noe E. Nnko - Bog - Taylor & Francis Ltd - Plusbog.dk

The book provides an accessible, comprehensive introduction for beginners to machine learning, equipping them with the fundamental skills and techniques essential for this field. It enables beginners to construct practical, real-world solutions powered by machine learning across diverse application domains. It demonstrates the fundamental techniques involved in data collection, integration, cleansing, transformation, development, and deployment of machine learning models. This book emphasizes the importance of integrating responsible and explainable AI into machine learning models, ensuring these principles are prioritized rather than treated as an afterthought. To support learning, this book also offers information on accessing additional machine learning resources such as datasets, libraries, pre-trained models, and tools for tracking machine learning models. This is a core resource for students and instructors of machine learning and data science looking for a beginner-friendly material which offers real-world applications and takes ethical discussions into account. The Open Access version of this book, available at http://www.taylorfrancis.com , has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives (CC-BY-NC-ND) 4.0 license.

DKK 514.00
1

Machine Learning in Multimedia - - Bog - Taylor & Francis Ltd - Plusbog.dk

Machine Learning in Multimedia - - Bog - Taylor & Francis Ltd - Plusbog.dk

This book explores the interdisciplinary nature of machine learning in multimedia, highlighting its intersections with fields such as computer vision, natural language processing, and audio signal processing. Machine Learning in Multimedia: Unlocking the Power of Visual and Auditory Intelligence serves as a comprehensive guide to navigating this exciting terrain where artificial intelligence meets the rich tapestry of visual and auditory data. At its core, this book seeks to unravel the mysteries and unveil the potential of machine learning in the realm of multimedia. Whether it''s enhancing user experiences in virtual environments, revolutionizing medical diagnostics, or shaping the future of entertainment, the impact of machine learning in multimedia is profound and far-reaching. The journey begins with a thorough exploration of the foundational principles of machine learning, providing readers with a solid understanding of algorithms, models, and techniques tailored specifically for multimedia data. Through clear explanations and illustrative examples, readers will gain insights into how machine learning algorithms can be trained to extract meaningful patterns and insights from diverse forms of multimedia content. Moving beyond theory, this book delves into practical implementations and real-world applications of machine learning in multimedia. Through a series of case studies and examples, readers will witness firsthand how machine learning algorithms are transforming industries and reshaping the way we interact with multimedia content. Whether it''s improving image recognition accuracy in autonomous vehicles, enabling personalized recommendations in streaming platforms, or enhancing speech recognition systems for better accessibility, the possibilities are limitless. This book will be helpful to computer science, data science, and artificial intelligence researchers, students, and professionals looking to unlock the full potential of visual and auditory intelligence through the power of machine learning.

DKK 759.00
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Digital Mayhem 3D Machine Techniques - - Bog - Taylor & Francis Ltd - Plusbog.dk

Digital Mayhem 3D Machine Techniques - - Bog - Taylor & Francis Ltd - Plusbog.dk

Inspire your own creativity with Digital Mayhem 3D Machine Techniques. Compiled by Duncan Evans, founder of 3D Artist Magazine , this book is the next installment of the Digital Mayhem series. Explore, page after page, spectacular machine images created by the industry’s top talent. With over 500 stunning images , Digital Mayhem 3D Machine Techniques is not your basic software manual. It is where technique and inspiration meet – a detailed and innovative guide to great machine images. Each chapter contains vibrant images and an explanation of the artist’s workflow, including a step-by-step tutorial to help you achieve similar results. From pre-viz to post production, this book takes you through the whole process of creating your very own machine masterpiece! - - Get inside the minds of the artists with breakdown overviews and supporting imagery to explain what went into their piece. - Expand your digital canvas to include a variety of software techniques, tools and workflows. - Visit the companion website for additional resources to help you develop your machine technique skills. -

DKK 488.00
1

Functional Reverse Engineering of Strategic and Non-Strategic Machine Tools - - Bog - Taylor & Francis Ltd - Plusbog.dk

Functional Reverse Engineering of Strategic and Non-Strategic Machine Tools - - Bog - Taylor & Francis Ltd - Plusbog.dk

This book describes capacity building in strategic and non-strategic machine tool technology. It includes machine building in sectors such as machine tools, automobiles, home appliances, energy, and biomedical engineering, along with case studies. The book offers guidelines for capacity building in academia, covering how to promote enterprises of functional reverse engineering enterprises. It also discusses machine tool development, engineering design, prototyping of strategic, and non-strategies machine tools, as well as presenting communication strategies and IoT, along with case studies. Professionals from the CNC (Computer Numeric Control) machine tools industry, industrial and manufacturing engineers, and students and faculty in engineering disciplines will find interest in this book.

DKK 486.00
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Introduction to Machine Learning and Bioinformatics - Theodore Perkins - Bog - Taylor & Francis Ltd - Plusbog.dk

Introduction to Machine Learning and Bioinformatics - Theodore Perkins - Bog - Taylor & Francis Ltd - Plusbog.dk

Lucidly Integrates Current Activities Focusing on both fundamentals and recent advances, Introduction to Machine Learning and Bioinformatics presents an informative and accessible account of the ways in which these two increasingly intertwined areas relate to each other. Examines Connections between Machine Learning & Bioinformatics The book begins with a brief historical overview of the technological developments in biology. It then describes the main problems in bioinformatics and the fundamental concepts and algorithms of machine learning. After forming this foundation, the authors explore how machine learning techniques apply to bioinformatics problems, such as electron density map interpretation, biclustering, DNA sequence analysis, and tumor classification. They also include exercises at the end of some chapters and offer supplementary materials on their website. Explores How Machine Learning Techniques Can Help Solve Bioinformatics Problems Shedding light on aspects of both machine learning and bioinformatics, this text shows how the innovative tools and techniques of machine learning help extract knowledge from the deluge of information produced by today’s biological experiments.

DKK 643.00
1

Stochastic Optimization for Large-scale Machine Learning - Vinod Kumar Chauhan - Bog - Taylor & Francis Ltd - Plusbog.dk

Stochastic Optimization for Large-scale Machine Learning - Vinod Kumar Chauhan - Bog - Taylor & Francis Ltd - Plusbog.dk

Advancements in the technology and availability of data sources have led to the `Big Data'' era. Working with large data offers the potential to uncover more fine-grained patterns and take timely and accurate decisions, but it also creates a lot of challenges such as slow training and scalability of machine learning models. One of the major challenges in machine learning is to develop efficient and scalable learning algorithms, i.e., optimization techniques to solve large scale learning problems. Stochastic Optimization for Large-scale Machine Learning identifies different areas of improvement and recent research directions to tackle the challenge. Developed optimisation techniques are also explored to improve machine learning algorithms based on data access and on first and second order optimisation methods. Key Features: - Bridges machine learning and Optimisation. - Bridges theory and practice in machine learning. - Identifies key research areas and recent research directions to solve large-scale machine learning problems. - Develops optimisation techniques to improve machine learning algorithms for big data problems. The book will be a valuable reference to practitioners and researchers as well as students in the field of machine learning.

DKK 542.00
1

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

Cost-Sensitive Machine Learning - - Bog - Taylor & Francis Ltd - 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.

DKK 637.00
1

Machine Learning and its Applications - Peter Wlodarczak - Bog - Taylor & Francis Ltd - Plusbog.dk

Machine Learning and its Applications - Peter Wlodarczak - Bog - Taylor & Francis Ltd - Plusbog.dk

In recent years, machine learning has gained a lot of interest. Due to the advances in processor technology and the availability of large amounts of data, machine learning techniques have provided astounding results in areas such as object recognition or natural language processing. New approaches, e.g. deep learning, have provided groundbreaking outcomes in fields such as multimedia mining or voice recognition. Machine learning is now used in virtually every domain and deep learning algorithms are present in many devices such as smartphones, cars, drones, healthcare equipment, or smart home devices. The Internet, cloud computing and the Internet of Things produce a tsunami of data and machine learning provides the methods to effectively analyze the data and discover actionable knowledge. This book describes the most common machine learning techniques such as Bayesian models, support vector machines, decision tree induction, regression analysis, and recurrent and convolutional neural networks. It first gives an introduction into the principles of machine learning. It then covers the basic methods including the mathematical foundations. The biggest part of the book provides common machine learning algorithms and their applications. Finally, the book gives an outlook into some of the future developments and possible new research areas of machine learning and artificial intelligence in general. This book is meant to be an introduction into machine learning. It does not require prior knowledge in this area. It covers some of the basic mathematical principle but intends to be understandable even without a background in mathematics. It can be read chapter wise and intends to be comprehensible, even when not starting in the beginning. Finally, it also intends to be a reference book. Key Features: Describes real world problems that can be solved using Machine Learning Provides methods for directly applying Machine Learning techniques to concrete real world problems Demonstrates how to apply Machine Learning techniques using different frameworks such as TensorFlow, MALLET, R

DKK 505.00
1

Statistical Machine Learning - Richard Golden - Bog - Taylor & Francis Ltd - Plusbog.dk

Statistical Machine Learning - Richard Golden - Bog - Taylor & Francis Ltd - Plusbog.dk

The recent rapid growth in the variety and complexity of new machine learning architectures requires the development of improved methods for designing, analyzing, evaluating, and communicating machine learning technologies. Statistical Machine Learning: A Unified Framework provides students, engineers, and scientists with tools from mathematical statistics and nonlinear optimization theory to become experts in the field of machine learning. In particular, the material in this text directly supports the mathematical analysis and design of old, new, and not-yet-invented nonlinear high-dimensional machine learning algorithms. Features: - - - Unified empirical risk minimization framework supports rigorous mathematical analyses of widely used supervised, unsupervised, and reinforcement machine learning algorithms - - - Matrix calculus methods for supporting machine learning analysis and design applications - - - Explicit conditions for ensuring convergence of adaptive, batch, minibatch, MCEM, and MCMC learning algorithms that minimize both unimodal and multimodal objective functions - - - Explicit conditions for characterizing asymptotic properties of M-estimators and model selection criteria such as AIC and BIC in the presence of possible model misspecification - This advanced text is suitable for graduate students or highly motivated undergraduate students in statistics, computer science, electrical engineering, and applied mathematics. The text is self-contained and only assumes knowledge of lower-division linear algebra and upper-division probability theory. Students, professional engineers, and multidisciplinary scientists possessing these minimal prerequisites will find this text challenging yet accessible. About the Author: Richard M. Golden (Ph.D., M.S.E.E., B.S.E.E.) is Professor of Cognitive Science and Participating Faculty Member in Electrical Engineering at the University of Texas at Dallas. Dr. Golden has published articles and given talks at scientific conferences on a wide range of topics in the fields of both statistics and machine learning over the past three decades. His long-term research interests include identifying conditions for the convergence of deterministic and stochastic machine learning algorithms and investigating estimation and inference in the presence of possibly misspecified probability models.

DKK 993.00
1

Entropy Randomization in Machine Learning - Yuri S. Popkov - Bog - Taylor & Francis Ltd - Plusbog.dk

Entropy Randomization in Machine Learning - Yuri S. Popkov - Bog - Taylor & Francis Ltd - Plusbog.dk

Entropy Randomization in Machine Learning presents a new approach to machine learning—entropy randomization—to obtain optimal solutions under uncertainty (uncertain data and models of the objects under study). Randomized machine-learning procedures involve models with random parameters and maximum entropy estimates of the probability density functions of the model parameters under balance conditions with measured data. Optimality conditions are derived in the form of nonlinear equations with integral components. A new numerical random search method is developed for solving these equations in a probabilistic sense. Along with the theoretical foundations of randomized machine learning, Entropy Randomization in Machine Learning considers several applications to binary classification, modelling the dynamics of the Earth’s population, predicting seasonal electric load fluctuations of power supply systems, and forecasting the thermokarst lakes area in Western Siberia. Features • A systematic presentation of the randomized machine-learning problem: from data processing, through structuring randomized models and algorithmic procedure, to the solution of applications-relevant problems in different fields • Provides new numerical methods for random global optimization and computation of multidimensional integrals • A universal algorithm for randomized machine learning This book will appeal to undergraduates and postgraduates specializing in artificial intelligence and machine learning, researchers and engineers involved in the development of applied machine learning systems, and researchers of forecasting problems in various fields.

DKK 856.00
1

Entropy Randomization in Machine Learning - Yuri S. Popkov - Bog - Taylor & Francis Ltd - Plusbog.dk

Entropy Randomization in Machine Learning - Yuri S. Popkov - Bog - Taylor & Francis Ltd - Plusbog.dk

Entropy Randomization in Machine Learning presents a new approach to machine learning—entropy randomization—to obtain optimal solutions under uncertainty (uncertain data and models of the objects under study). Randomized machine-learning procedures involve models with random parameters and maximum entropy estimates of the probability density functions of the model parameters under balance conditions with measured data. Optimality conditions are derived in the form of nonlinear equations with integral components. A new numerical random search method is developed for solving these equations in a probabilistic sense. Along with the theoretical foundations of randomized machine learning, Entropy Randomization in Machine Learning considers several applications to binary classification, modelling the dynamics of the Earth’s population, predicting seasonal electric load fluctuations of power supply systems, and forecasting the thermokarst lakes area in Western Siberia. Features • A systematic presentation of the randomized machine-learning problem: from data processing, through structuring randomized models and algorithmic procedure, to the solution of applications-relevant problems in different fields • Provides new numerical methods for random global optimization and computation of multidimensional integrals • A universal algorithm for randomized machine learning This book will appeal to undergraduates and postgraduates specializing in artificial intelligence and machine learning, researchers and engineers involved in the development of applied machine learning systems, and researchers of forecasting problems in various fields.

DKK 467.00
1