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Machine Learning for Managers

Machine Learning for Managers

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. | Machine Learning for Managers

GBP 29.99
1

Applied Machine Learning for Smart Data Analysis

Applied Machine Learning for Smart Data Analysis

The book focuses on how machine learning and the Internet of Things (IoT) has empowered the advancement of information driven arrangements including key concepts and advancements. Ontologies that are used in heterogeneous IoT environments have been discussed including interpretation context awareness analyzing various data sources machine learning algorithms and intelligent services and applications. Further it includes unsupervised and semi-supervised machine learning techniques with study of semantic analysis and thorough analysis of reviews. Divided into sections such as machine learning security IoT and data mining the concepts are explained with practical implementation including results. Key Features Follows an algorithmic approach for data analysis in machine learning Introduces machine learning methods in applications Address the emerging issues in computing such as deep learning machine learning Internet of Things and data analytics Focuses on machine learning techniques namely unsupervised and semi-supervised for unseen and seen data sets Case studies are covered relating to human health transportation and Internet applications | Applied Machine Learning for Smart Data Analysis

GBP 125.00
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Machine Learning for Neuroscience A Systematic Approach

Machine Learning for Neuroscience A Systematic Approach

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. | Machine Learning for Neuroscience A Systematic Approach

GBP 82.99
1

Stochastic Optimization for Large-scale Machine Learning

Stochastic Optimization for Large-scale Machine Learning

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.

GBP 150.00
1

Green Machine Learning Protocols for Future Communication Networks

Green Machine Learning Protocols for Future Communication Networks

Machine learning has shown tremendous benefits in solving complex network problems and providing situation and parameter prediction. However heavy resources are required to process and analyze the data which can be done either offline or using edge computing but also requires heavy transmission resources to provide a timely response. The need here is to provide lightweight machine learning protocols that can process and analyze the data at run time and provide a timely and efficient response. These algorithms have grown in terms of computation and memory requirements due to the availability of large data sets. These models/algorithms also require high levels of resources such as computing memory communication and storage. The focus so far was on producing highly accurate models for these communication networks without considering the energy consumption of these machine learning algorithms. For future scalable and sustainable network applications efforts are required toward designing new machine learning protocols and modifying the existing ones which consume less energy i. e. green machine learning protocols. In other words novel and lightweight green machine learning algorithms/protocols are required to reduce energy consumption which can also reduce the carbon footprint. To realize the green machine learning protocols this book presents different aspects of green machine learning for future communication networks. This book highlights mainly the green machine learning protocols for cellular communication federated learning-based models and protocols for Beyond Fifth Generation networks approaches for cloud-based communications and Internet-of-Things. This book also highlights the design considerations and challenges for green machine learning protocols for different future applications. | Green Machine Learning Protocols for Future Communication Networks

GBP 110.00
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Machine Learning for Healthcare Handling and Managing Data

Machine Learning for Healthcare Handling and Managing Data

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. | Machine Learning for Healthcare Handling and Managing Data

GBP 115.00
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Machine Learning for Decision Sciences with Case Studies in Python

Machine Learning for Decision Sciences with Case Studies in Python

This book provides a detailed description of machine learning algorithms in data analytics data science life cycle Python for machine learning linear regression logistic regression and so forth. It addresses the concepts of machine learning in a practical sense providing complete code and implementation for real-world examples in electrical oil and gas e-commerce and hi-tech industries. The focus is on Python programming for machine learning and patterns involved in decision science for handling data. Features: Explains the basic concepts of Python and its role in machine learning. Provides comprehensive coverage of feature engineering including real-time case studies. Perceives the structural patterns with reference to data science and statistics and analytics. Includes machine learning-based structured exercises. Appreciates different algorithmic concepts of machine learning including unsupervised supervised and reinforcement learning. This book is aimed at researchers professionals and graduate students in data science machine learning computer science and electrical and computer engineering. | Machine Learning for Decision Sciences with Case Studies in Python

GBP 160.00
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Bayesian Reasoning and Gaussian Processes for Machine Learning Applications

Bayesian Reasoning and Gaussian Processes for Machine Learning Applications

This book introduces Bayesian reasoning and Gaussian processes into machine learning applications. Bayesian methods are applied in many areas such as game development decision making and drug discovery. It is very effective for machine learning algorithms in handling missing data and extracting information from small datasets. Bayesian Reasoning and Gaussian Processes for Machine Learning Applications uses a statistical background to understand continuous distributions and how learning can be viewed from a probabilistic framework. The chapters progress into such machine learning topics as belief network and Bayesian reinforcement learning which is followed by Gaussian process introduction classification regression covariance and performance analysis of Gaussian processes with other models. FEATURES Contains recent advancements in machine learning Highlights applications of machine learning algorithms Offers both quantitative and qualitative research Includes numerous case studies This book is aimed at graduates researchers and professionals in the field of data science and machine learning.

GBP 105.00
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Machine Learning for Business Analytics Real-Time Data Analysis for Decision-Making

Machine Learning for Business Analytics Real-Time Data Analysis for Decision-Making

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. | Machine Learning for Business Analytics Real-Time Data Analysis for Decision-Making

GBP 48.99
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Machine Learning for Sustainable Manufacturing in Industry 4.0 Concept Concerns and Applications

Machine Learning for Sustainable Manufacturing in Industry 4.0 Concept Concerns and Applications

The book focuses on the recent developments in the areas of error reduction resource optimization and revenue growth in sustainable manufacturing using machine learning. It presents the integration of smart technologies such as machine learning in the field of Industry 4. 0 for better quality products and efficient manufacturing methods. Focusses on machine learning applications in Industry 4. 0 ecosystem such as resource optimization data analysis and predictions. Highlights the importance of the explainable machine learning model in the manufacturing processes. Presents the integration of machine learning and big data analytics from an industry 4. 0 perspective. Discusses advanced computational techniques for sustainable manufacturing. Examines environmental impacts of operations and supply chain from an industry 4. 0 perspective. This book provides scientific and technological insight into sustainable manufacturing by covering a wide range of machine learning applications fault detection cyber-attack prediction and inventory management. It further discusses resource optimization using machine learning in industry 4. 0 and explainable machine learning models for industry 4. 0. It will serve as an ideal reference text for senior undergraduate graduate students and academic researchers in the fields including mechanical engineering manufacturing engineering production engineering aerospace engineering and computer engineering. | Machine Learning for Sustainable Manufacturing in Industry 4. 0 Concept Concerns and Applications

GBP 110.00
1

Machine Learning for Social and Behavioral Research

Machine Learning for Social and Behavioral Research

Today's social and behavioral researchers increasingly need to know: "What do I do with all this data?" This book provides the skills needed to analyze and report large complex data sets using machine learning tools and to understand published machine learning articles. Techniques are demonstrated using actual data (Big Five Inventory early childhood learning and more) with a focus on the interplay of statistical algorithm data and theory. The identification of heterogeneity measurement error regularization and decision trees are also emphasized. The book covers basic principles as well as a range of methods for analyzing univariate and multivariate data (factor analysis structural equation models and mixed-effects models). Analysis of text and social network data is also addressed. End-of-chapter "Computational Time and Resources" sections include discussions of key R packages; the companion website provides R programming scripts and data for the book's examples. | Machine Learning for Social and Behavioral Research

GBP 51.99
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Handbook of Machine Learning for Computational Optimization Applications and Case Studies

Handbook of Machine Learning for Computational Optimization Applications and Case Studies

Technology is moving at an exponential pace in this era of computational intelligence. Machine learning has emerged as one of the most promising tools used to challenge and think beyond current limitations. This handbook will provide readers with a leading edge to improving their products and processes through optimal and smarter machine learning techniques. This handbook focuses on new machine learning developments that can lead to newly developed applications. It uses a predictive and futuristic approach which makes machine learning a promising tool for processes and sustainable solutions. It also promotes newer algorithms that are more efficient and reliable for new dimensions in discovering other applications and then goes on to discuss the potential in making better use of machines in order to ensure optimal prediction execution and decision-making. Individuals looking for machine learning-based knowledge will find interest in this handbook. The readership ranges from undergraduate students of engineering and allied courses to researchers professionals and application designers. | Handbook of Machine Learning for Computational Optimization Applications and Case Studies

GBP 150.00
1

Machine Learning for Criminology and Crime Research At the Crossroads

Machine Learning for Criminology and Crime Research At the Crossroads

Machine Learning for Criminology and Crime Research: At the Crossroads reviews the roots of the intersection between machine learning artificial intelligence (AI) and research on crime; examines the current state of the art in this area of scholarly inquiry; and discusses future perspectives that may emerge from this relationship. As machine learning and AI approaches become increasingly pervasive it is critical for criminology and crime research to reflect on the ways in which these paradigms could reshape the study of crime. In response this book seeks to stimulate this discussion. The opening part is framed through a historical lens with the first chapter dedicated to the origins of the relationship between AI and research on crime refuting the novelty narrative that often surrounds this debate. The second presents a compact overview of the history of AI further providing a nontechnical primer on machine learning. The following chapter reviews some of the most important trends in computational criminology and quantitatively characterizing publication patterns at the intersection of AI and criminology through a network science approach. This book also looks to the future proposing two goals and four pathways to increase the positive societal impact of algorithmic systems in research on crime. The sixth chapter provides a survey of the methods emerging from the integration of machine learning and causal inference showcasing their promise for answering a range of critical questions. With its transdisciplinary approach Machine Learning for Criminology and Crime Research is important reading for scholars and students in criminology criminal justice sociology and economics as well as AI data sciences and statistics and computer science. | Machine Learning for Criminology and Crime Research At the Crossroads

GBP 130.00
1

Machine Learning and Deep Learning Techniques for Medical Image Recognition

Machine Learning and Deep Learning Techniques for Medical Image Recognition

Machine Learning and Deep Learning Techniques for Medical Image Recognition comprehensively reviews deep learning-based algorithms in medical image analysis problems including medical image processing. It includes a detailed review of deep learning approaches for semantic object detection and segmentation in medical image computing and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks with the theory and varied selection of techniques for semantic segmentation using deep learning principles in medical imaging supported by practical examples. Features: Offers important key aspects in the development and implementation of machine learning and deep learning approaches toward developing prediction tools and models and improving medical diagnosis Teaches how machine learning and deep learning algorithms are applied to a broad range of application areas including chest X-ray breast computer-aided detection lung and chest microscopy and pathology Covers common research problems in medical image analysis and their challenges Focuses on aspects of deep learning and machine learning for combating COVID-19 Includes pertinent case studies This book is aimed at researchers and graduate students in computer engineering artificial intelligence and machine learning and biomedical imaging.

GBP 110.00
1

Machine Learning for the Physical Sciences Fundamentals and Prototyping with Julia

Machine Learning for the Physical Sciences Fundamentals and Prototyping with Julia

Machine learning is an exciting topic with a myriad of applications. However most textbooks are targeted towards computer science students. This however creates a complication for scientists across the physical sciences that also want to understand the main concepts of machine learning and look ahead to applica- tions and advancements in their fields. This textbook bridges this gap providing an introduction to the mathematical foundations for the main algorithms used in machine learning for those from the physical sciences without a formal background in computer science. It demon- strates how machine learning can be used to solve problems in physics and engineering targeting senior undergraduate and graduate students in physics and electrical engineering alongside advanced researchers. All codes are available on the author's website: C•Lab (nau. edu) They are also available on GitHub: https://github. com/StxGuy/MachineLearning Key Features: Includes detailed algorithms. Supplemented by codes in Julia: a high-performing language and one that is easy to read for those in the natural sciences. All algorithms are presented with a good mathematical background. | Machine Learning for the Physical Sciences Fundamentals and Prototyping with Julia

GBP 64.99
1

Machine Learning for Factor Investing Python Version

Machine Learning for Factor Investing Python Version

Machine learning (ML) is progressively reshaping the fields of quantitative finance and algorithmic trading. ML tools are increasingly adopted by hedge funds and asset managers notably for alpha signal generation and stocks selection. The technicality of the subject can make it hard for non-specialists to join the bandwagon as the jargon and coding requirements may seem out-of-reach. Machine learning for factor investing: Python version bridges this gap. It provides a comprehensive tour of modern ML-based investment strategies that rely on firm characteristics. The book covers a wide array of subjects which range from economic rationales to rigorous portfolio back-testing and encompass both data processing and model interpretability. Common supervised learning algorithms such as tree models and neural networks are explained in the context of style investing and the reader can also dig into more complex techniques like autoencoder asset returns Bayesian additive trees and causal models. All topics are illustrated with self-contained Python code samples and snippets that are applied to a large public dataset that contains over 90 predictors. The material is available online so that readers can reproduce and enhance the examples at their convenience. If you have even a basic knowledge of quantitative finance this combination of theoretical concepts and practical illustrations will help you learn quickly and deepen your financial and technical expertise. | Machine Learning for Factor Investing Python Version

GBP 66.99
1

Video Based Machine Learning for Traffic Intersections

Video Based Machine Learning for Traffic Intersections

Video Based Machine Learning for Traffic Intersections describes the development of computer vision and machine learning-based applications for Intelligent Transportation Systems (ITS) and the challenges encountered during their deployment. This book presents several novel approaches including a two-stream convolutional network architecture for vehicle detection tracking and near-miss detection; an unsupervised approach to detect near-misses in fisheye intersection videos using a deep learning model combined with a camera calibration and spline-based mapping method; and algorithms that utilize video analysis and signal timing data to accurately detect and categorize events based on the phase and type of conflict in pedestrian-vehicle and vehicle-vehicle interactions. The book makes use of a real-time trajectory prediction approach combined with aligned Google Maps information to estimate vehicle travel time across multiple intersections. Novel visualization software designed by the authors to serve traffic practitioners is used to analyze the efficiency and safety of intersections. The software offers two modes: a streaming mode and a historical mode both of which are useful to traffic engineers who need to quickly analyze trajectories to better understand traffic behavior at an intersection. Overall this book presents a comprehensive overview of the application of computer vision and machine learning to solve transportation-related problems. Video Based Machine Learning for Traffic Intersections demonstrates how these techniques can be used to improve safety efficiency and traffic flow as well as identify potential conflicts and issues before they occur. The range of novel approaches and techniques presented offers a glimpse of the exciting possibilities that lie ahead for ITS research and development. Key Features: Describes the development and challenges associated with Intelligent Transportation Systems (ITS) Provides novel visualization software designed to serve traffic practitioners in analyzing the efficiency and safety of an intersection Has the potential to proactively identify potential conflict situations and develop an early warning system for real-time vehicle-vehicle and pedestrian-vehicle conflicts

GBP 99.99
1

Artificial Intelligence and Machine Learning for Business for Non-Engineers

Artificial Intelligence and Machine Learning for Business for Non-Engineers

The next big area within the information and communication technology field is Artificial Intelligence (AI). The industry is moving to automate networks cloud-based systems (e. g. Salesforce) databases (e. g. Oracle) AWS machine learning (e. g. Amazon Lex) and creating infrastructure that has the ability to adapt in real-time to changes and learn what to anticipate in the future. It is an area of technology that is coming faster and penetrating more areas of business than any other in our history. AI will be used from the C-suite to the distribution warehouse floor. Replete with case studies this book provides a working knowledge of AI’s current and future capabilities and the impact it will have on every business. It covers everything from healthcare to warehousing banking finance and education. It is essential reading for anyone involved in industry. | Artificial Intelligence and Machine Learning for Business for Non-Engineers

GBP 56.99
1

Human-Machine Interaction and IoT Applications for a Smarter World

Human-Machine Interaction and IoT Applications for a Smarter World

Human-Machine Interaction and IoT Applications for a Smarter World explores the futuristic trends at the cutting edge of study and research on Human-Machine Interaction (HMI) which is also known as Human-Computer Interface (HCI) and the Internet of Things (IoT) by featuring applications in a proficient adaptable and manageable way. It covers the mainstays of the IoT world through a thorough description of the present advancements systems and structures. This book: Discusses algorithms and design methodologies for the implementation of HMI based IoT systems. Covers real-time utility of IoT-based devices and systems. Provides human-machine interactive technologies and smart applications using IoT. Covers cyber-physical systems and IoT in HMI using a blend of theoretical knowledge with a practical approach. It also covers important concepts including smart grid and energy consumption monitoring smart vehicular and transportation systems smart home automation automatic identification systems supervisory control and data acquisition systems designing and integrating heterogeneous Human-Machine interactions virtual and augmented reality natural language processing computer vision and automatic speech recognition. This text will be useful for senior undergraduate graduate students and academic researchers in areas including electrical electronics and communications engineering as well as computer science.

GBP 130.00
1

Machine Learning Concepts Techniques and Applications

Machine Learning Concepts Techniques and Applications

Machine Learning: Concepts Techniques and Applications starts at basic conceptual level of explaining machine learning and goes on to explain the basis of machine learning algorithms. The mathematical foundations required are outlined along with their associations to machine learning. The book then goes on to describe important machine learning algorithms along with appropriate use cases. This approach enables the readers to explore the applicability of each algorithm by understanding the differences between them. A comprehensive account of various aspects of ethical machine learning has been discussed. An outline of deep learning models is also included. The use cases self-assessments exercises activities numerical problems and projects associated with each chapter aims to concretize the understanding. Features Concepts of Machine learning from basics to algorithms to implementation Comparison of Different Machine Learning Algorithms – When to use them & Why – for Application developers and Researchers Machine Learning from an Application Perspective – General & Machine learning for Healthcare Education Business Engineering Applications Ethics of machine learning including Bias Fairness Trust Responsibility Basics of Deep learning important deep learning models and applications Plenty of objective questions Use Cases Activity and Project based Learning Exercises The book aims to make the thinking of applications and problems in terms of machine learning possible for graduate students researchers and professionals so that they can formulate the problems prepare data decide features select appropriate machine learning algorithms and do appropriate performance evaluation. | Machine Learning Concepts Techniques and Applications

GBP 140.00
1

Intelligent Prognostics for Engineering Systems with Machine Learning Techniques

Intelligent Prognostics for Engineering Systems with Machine Learning Techniques

The text discusses the latest data-driven physics-based and hybrid approaches employed in each stage of industrial prognostics and reliability estimation. It will be a useful text for senior undergraduate graduate students and academic researchers in areas such as industrial and production engineering electrical engineering and computer science. The book Discusses basic as well as advance research in the field of prognostics Explores integration of data collection fault detection degradation modeling and reliability prediction in one volume Covers prognostics and health management (PHM) of engineering systems Discusses latest approaches in the field of prognostics based on machine learning The text deals with tools and techniques used to predict/ extrapolate/ forecast the process behavior based on current health state assessment and future operating conditions with the help of Machine learning. It will serve as a useful reference text for senior undergraduate graduate students and academic researchers in areas such as industrial and production engineering manufacturing science electrical engineering and computer science. | Intelligent Prognostics for Engineering Systems with Machine Learning Techniques

GBP 110.00
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Transformers for Machine Learning A Deep Dive

Transformers for Machine Learning A Deep Dive

Transformers are becoming a core part of many neural network architectures employed in a wide range of applications such as NLP Speech Recognition Time Series and Computer Vision. Transformers have gone through many adaptations and alterations resulting in newer techniques and methods. Transformers for Machine Learning: A Deep Dive is the first comprehensive book on transformers. Key Features: A comprehensive reference book for detailed explanations for every algorithm and techniques related to the transformers. 60+ transformer architectures covered in a comprehensive manner. A book for understanding how to apply the transformer techniques in speech text time series and computer vision. Practical tips and tricks for each architecture and how to use it in the real world. Hands-on case studies and code snippets for theory and practical real-world analysis using the tools and libraries all ready to run in Google Colab. The theoretical explanations of the state-of-the-art transformer architectures will appeal to postgraduate students and researchers (academic and industry) as it will provide a single entry point with deep discussions of a quickly moving field. The practical hands-on case studies and code will appeal to undergraduate students practitioners and professionals as it allows for quick experimentation and lowers the barrier to entry into the field. | Transformers for Machine Learning A Deep Dive

GBP 44.99
1

Entropy Randomization in Machine Learning

Entropy Randomization in Machine Learning

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.

GBP 82.99
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Cost-Sensitive Machine Learning

Cost-Sensitive Machine Learning

In machine learning applications practitioners must take into account the cost associated with the algorithm. These costs include: Cost of acquiring training dataCost of data annotation/labeling and cleaningComputational cost for model fitting validation and testingCost of collecting features/attributes for test dataCost of user feedback collectionCost of incorrect prediction/classificationCost-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.

GBP 59.99
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Feature Engineering for Machine Learning and Data Analytics

Feature Engineering for Machine Learning and Data Analytics

Feature engineering plays a vital role in big data analytics. Machine learning and data mining algorithms cannot work without data. Little can be achieved if there are few features to represent the underlying data objects and the quality of results of those algorithms largely depends on the quality of the available features. Feature Engineering for Machine Learning and Data Analytics provides a comprehensive introduction to feature engineering including feature generation feature extraction feature transformation feature selection and feature analysis and evaluation. The book presents key concepts methods examples and applications as well as chapters on feature engineering for major data types such as texts images sequences time series graphs streaming data software engineering data Twitter data and social media data. It also contains generic feature generation approaches as well as methods for generating tried-and-tested hand-crafted domain-specific features. The first chapter defines the concepts of features and feature engineering offers an overview of the book and provides pointers to topics not covered in this book. The next six chapters are devoted to feature engineering including feature generation for specific data types. The subsequent four chapters cover generic approaches for feature engineering namely feature selection feature transformation based feature engineering deep learning based feature engineering and pattern based feature generation and engineering. The last three chapters discuss feature engineering for social bot detection software management and Twitter-based applications respectively. This book can be used as a reference for data analysts big data scientists data preprocessing workers project managers project developers prediction modelers professors researchers graduate students and upper level undergraduate students. It can also be used as the primary text for courses on feature engineering or as a supplement for courses on machine learning data mining and big data analytics.

GBP 44.99
1