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Knowledge Engineering, Machine Learning and Lattice Computing with Applications - - Bog - Springer-Verlag Berlin and Heidelberg GmbH & Co. KG -

Knowledge Engineering, Machine Learning and Lattice Computing with Applications - - Bog - Springer-Verlag Berlin and Heidelberg GmbH & Co. KG -

This book constitutes the refereed proceedings of the 16th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2012, held in San Sebastian, Spain, in September 2012. The 20 revised full papers presented were carefully reviewed and selected from 130 submissions. The papers are organized in topical sections on bioinspired and machine learning methods, machine learning applications, semantics and ontology based techniques, and lattice computing and games.

DKK 385.00
1

Machine Learning in Medical Imaging - - Bog - Springer-Verlag Berlin and Heidelberg GmbH & Co. KG - Plusbog.dk

Machine Learning in Medical Imaging - - Bog - Springer-Verlag Berlin and Heidelberg GmbH & Co. KG - Plusbog.dk

This book constitutes the refereed proceedings of the Third International Workshop on Machine Learning in Medical Imaging, MLMI 2012, held in conjunction with MICCAI 2012, in Nice, France, in October 2012. The 33 revised full papers presented were carefully reviewed and selected from 67 submissions. The main aim of this workshop is to help advance the scientific research within the broad field of machine learning in medical imaging. It focuses on major trends and challenges in this area, and it presents work aimed to identify new cutting-edge techniques and their use in medical imaging.

DKK 385.00
1

Introduction to Data Governance for Machine Learning Systems - Aditya Nandan Prasad - Bog - Springer-Verlag Berlin and Heidelberg GmbH & Co. KG -

Introduction to Data Governance for Machine Learning Systems - Aditya Nandan Prasad - Bog - Springer-Verlag Berlin and Heidelberg GmbH & Co. KG -

This book is the first comprehensive guide to the intersection of data governance and machine learning (ML) projects. As ML applications proliferate, the quality, reliability, and ethical use of data is central to their success, which gives ML data governance unprecedented significance. However, adapting data governance principles to ML systems presents unique, complex challenges. Author Aditya Nandan Prasad equips you with the knowledge and tools needed to navigate this dynamic landscape effectively. Through this guide, you will learn to implement robust and responsible data governance practices, ensuring the development of sustainable, ethical, and future-proofed AI applications. The book begins by covering fundamental principles and practices of underlying ML applications and data governance before diving into the unique challenges and opportunities at play when adapting data governance theory and practice to ML projects, including establishing governance frameworks, ensuring data quality and interpretability, preprocessing, and the ethical implications of ML algorithms and techniques, from mitigating bias in AI systems to the importance of transparency in models. Monitoring and maintaining ML systems performance is also covered in detail, along with regulatory compliance and risk management considerations. Moreover, the book explores strategies for fostering a data-driven culture within organizations and offers guidance on change management to ensure successful adoption of data governance initiatives. Looking ahead, the book examines future trends and emerging challenges in ML data governance, such as Explainable AI (XAI) and the increasing complexity of data. What You Will LearnComprehensive understanding of machine learning and data governance, including fundamental principles, critical practices, and emerging challengesNavigating the complexities of managing data effectively within the context of machine learning projectsPractical strategies and best practices for implementing effective data governance in machine learning projectsKey aspects such as data quality, privacy, security, and ethical considerations, ensuring responsible and effective use of dataPreparation for the evolving landscape of ML data governance with a focus on future trends and emerging challenges in the rapidly evolving field of AI and machine learning Who This Book Is ForData professionals, including data scientists, data engineers, AI developers, or data governance specialists, as well as managers or decision makers looking to implement or improve data governance practices for machine learning projects

DKK 332.00
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Data Engineering for Machine Learning Pipelines - Pavan Kumar Narayanan - Bog - Springer-Verlag Berlin and Heidelberg GmbH & Co. KG - Plusbog.dk

Data Engineering for Machine Learning Pipelines - Pavan Kumar Narayanan - Bog - Springer-Verlag Berlin and Heidelberg GmbH & Co. KG - Plusbog.dk

This book covers modern data engineering functions and important Python libraries, to help you develop state-of-the-art ML pipelines and integration code. The book begins by explaining data analytics and transformation, delving into the Pandas library, its capabilities, and nuances. It then explores emerging libraries such as Polars and CuDF, providing insights into GPU-based computing and cutting-edge data manipulation techniques. The text discusses the importance of data validation in engineering processes, introducing tools such as Great Expectations and Pandera to ensure data quality and reliability. The book delves into API design and development, with a specific focus on leveraging the power of FastAPI. It covers authentication, authorization, and real-world applications, enabling you to construct efficient and secure APIs using FastAPI. Also explored is concurrency in data engineering, examining Dask's capabilities from basic setup to crafting advanced machine learning pipelines. The book includes development and delivery of data engineering pipelines using leading cloud platforms such as AWS, Google Cloud, and Microsoft Azure. The concluding chapters concentrate on real-time and streaming data engineering pipelines, emphasizing Apache Kafka and workflow orchestration in data engineering. Workflow tools such as Airflow and Prefect are introduced to seamlessly manage and automate complex data workflows. What sets this book apart is its blend of theoretical knowledge and practical application, a structured path from basic to advanced concepts, and insights into using state-of-the-art tools. With this book, you gain access to cutting-edge techniques and insights that are reshaping the industry. This book is not just an educational tool. It is a career catalyst, and an investment in your future as a data engineering expert, poised to meet the challenges of today's data-driven world. What You Will LearnElevate your data wrangling jobs by utilizing the power of both CPU and GPU computing, and learn to process data using Pandas 2.0, Polars, and CuDF at unprecedented speedsDesign data validation pipelines, construct efficient data service APIs, develop real-time streaming pipelines and master the art of workflow orchestration to streamline your engineering projectsLeverage concurrent programming to develop machine learning pipelines and get hands-on experience in development and deployment of machine learning pipelines across AWS, GCP, and Azure Who This Book Is ForData analysts, data engineers, data scientists, machine learning engineers, and MLOps specialists

DKK 519.00
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Machine Learning for Engineers - Marcus Neuer - Bog - Springer-Verlag Berlin and Heidelberg GmbH & Co. KG - Plusbog.dk

Machine Learning for Engineers - Marcus Neuer - Bog - Springer-Verlag Berlin and Heidelberg GmbH & Co. KG - Plusbog.dk

Machine learning and artificial intelligence are ubiquitous terms for improving technical processes. However, practical implementation in real-world problems is often difficult and complex. This textbook explains learning methods based on analytical concepts in conjunction with complete programming examples in Python, always referring to real technical application scenarios. It demonstrates the use of physics-informed learning strategies, the incorporation of uncertainty into modeling, and the development of explainable, trustworthy artificial intelligence with the help of specialized databases. Therefore, this textbook is aimed at students of engineering, natural science, medicine, and business administration as well as practitioners from industry (especially data scientists), developers of expert databases, and software developers.

DKK 434.00
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AI Solutions for the United Nations Sustainable Development Goals (UN SDGs) - Lavesh Babooram - Bog - Springer-Verlag Berlin and Heidelberg GmbH & Co.

AI Solutions for the United Nations Sustainable Development Goals (UN SDGs) - Lavesh Babooram - Bog - Springer-Verlag Berlin and Heidelberg GmbH & Co.

Learn the United Nations Sustainable Development Goals (UN SDGs) and see how machine learning can significantly contribute to their realization. This book imparts both theoretical knowledge and hands-on experience in comprehending and constructing machine learning-based applications for addressing multiple UN SDGs using JavaScript. The reading begins with a delineation of diverse UN SDG targets, providing an overview of previous successful applications of machine learning in solving realistic problems aligned with these targets. It thoroughly explains fundamental concepts of machine learning algorithms for prediction and classification, coupled with their implementation in JavaScript and HTML programming. Detailed case studies examine challenges related to renewable energy, agriculture, food production, health, environment, climate change, water quality, air quality, and telecommunications, corresponding to various UN SDGs. Each case study includes related works, datasets, machine learning algorithms, programming concepts, and comprehensive explanations of JavaScript and HTML codes used for web-based machine learning applications. The results obtained are meticulously analyzed and discussed, showcasing the pivotal role of machine learning in advancing the relevant SDGs. By the end of this book, you’ll have a firm understanding of SDG fundamentals and the practical application of machine learning to address diverse challenges associated with these goals. What You’ll LearnUnderstand the fundamental concepts of the UN SDGs, AI, and machine learning algorithms. Employ the correct machine learning algorithms to address challenges on the United Nations Sustainable Development Goals (UN SDGs)?Develop web-based machine learning applications for the UN SDGs using Javascript, and HTML. Analyze the impact of a machine learning-based solution on a specific UN SDG. Who This Book Is ForData scientists, machine learning engineers, software professionals, researchers, and graduate students.

DKK 519.00
1

MLOps with Ray - Hien Luu - Bog - Springer-Verlag Berlin and Heidelberg GmbH & Co. KG - Plusbog.dk

MLOps with Ray - Hien Luu - Bog - Springer-Verlag Berlin and Heidelberg GmbH & Co. KG - Plusbog.dk

Understand how to use MLOps as an engineering discipline to help with the challenges of bringing machine learning models to production quickly and consistently. This book will help companies worldwide to adopt and incorporate machine learning into their processes and products to improve their competitiveness. The book delves into this engineering discipline's aspects and components and explores best practices and case studies. Adopting MLOps requires a sound strategy, which the book's early chapters cover in detail. The book also discusses the infrastructure and best practices of Feature Engineering, Model Training, Model Serving, and Machine Learning Observability. Ray, the open source project that provides a unified framework and libraries to scale machine learning workload and the Python application, is introduced, and you will see how it fits into the MLOps technical stack. This book is intended for machine learning practitioners, such as machine learning engineers, and data scientists, who wish to help their company by adopting, building maps, and practicing MLOps. What You'll LearnGain an understanding of the MLOps disciplineKnow the MLOps technical stack and its componentsGet familiar with the MLOps adoption strategyUnderstand feature engineering Who This Book Is ForMachine learning practitioners, data scientists, and software engineers who are focusing on building machine learning systems and infrastructure to bring ML models to production

DKK 434.00
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Neural Networks with TensorFlow and Keras - Philip Hua - Bog - Springer-Verlag Berlin and Heidelberg GmbH & Co. KG - Plusbog.dk

Neural Networks with TensorFlow and Keras - Philip Hua - Bog - Springer-Verlag Berlin and Heidelberg GmbH & Co. KG - Plusbog.dk

Explore the capabilities of machine learning and neural networks. This comprehensive guidebook is tailored for professional programmers seeking to deepen their understanding of neural networks, machine learning techniques, and large language models (LLMs). The book explores the core of machine learning techniques, covering essential topics such as data pre-processing, model selection, and customization. It provides a robust foundation in neural network fundamentals, supplemented by practical case studies and projects. You will explore various network topologies, including Deep Neural Networks (DNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) networks, Variational Autoencoders (VAE), Generative Adversarial Networks (GAN), and Large Language Models (LLMs). Each concept is explained with clear, step-by-step instructions and accompanied by Python code examples using the latest versions of TensorFlow and Keras, ensuring a hands-on learning experience. By the end of this book, you will gain practical skills to apply these techniques to solving problems. Whether you are looking to advance your career or enhance your programming capabilities, this book provides the tools and knowledge needed to excel in the rapidly evolving field of machine learning and neural networks. What You Will LearnGrasp the fundamentals of various neural network topologies, including DNN, RNN, LSTM, VAE, GAN, and LLMsImplement neural networks using the latest versions of TensorFlow and Keras, with detailed Python code examplesKnow the techniques for data pre-processing, model selection, and customization to optimize machine learning modelsApply machine learning and neural network techniques in various professional scenarios Who This Book Is ForData scientists, machine learning enthusiasts, and software developers who wish to deepen their understanding of neural networks and machine learning techniques

DKK 391.00
1

Logic for Learning - John W. Lloyd - Bog - Springer-Verlag Berlin and Heidelberg GmbH & Co. KG - Plusbog.dk

Logic for Learning - John W. Lloyd - Bog - Springer-Verlag Berlin and Heidelberg GmbH & Co. KG - Plusbog.dk

This book provides a systematic approach to knowledge representation, computation, and learning using higher-order logic. For those interested in computational logic, it provides a framework for knowledge representation and computation based on higher-order logic, and demonstrates its advantages over more standard approaches based on first-order logic. For those interested in machine learning, the book explains how higher-order logic provides suitable knowledge representation formalisms and hypothesis languages for machine learning applications.

DKK 434.00
1

Beginning Anomaly Detection Using Python-Based Deep Learning - Suman Kalyan Adari - Bog - Springer-Verlag Berlin and Heidelberg GmbH & Co. KG -

Beginning Anomaly Detection Using Python-Based Deep Learning - Suman Kalyan Adari - Bog - Springer-Verlag Berlin and Heidelberg GmbH & Co. KG -

This beginner-oriented book will help you understand and perform anomaly detection by learning cutting-edge machine learning and deep learning techniques. This updated second edition focuses on supervised, semi-supervised, and unsupervised approaches to anomaly detection. Over the course of the book, you will learn how to use Keras and PyTorch in practical applications. It also introduces new chapters on GANs and transformers to reflect the latest trends in deep learning. Beginning Anomaly Detection Using Python-Based Deep Learning begins with an introduction to anomaly detection, its importance, and its applications. It then covers core data science and machine learning modeling concepts before delving into traditional machine learning algorithms such as OC-SVM and Isolation Forest for anomaly detection using scikit-learn. Following this, the authors explain the essentials of machine learning and deep learning, and how to implement multilayer perceptrons for supervised anomaly detection in both Keras and PyTorch. From here, the focus shifts to the applications of deep learning models for anomaly detection, including various types of autoencoders, recurrent neural networks (via LSTM), temporal convolutional networks, and transformers, with the latter three architectures applied to time-series anomaly detection. This edition has a new chapter on GANs (Generative Adversarial Networks), as well as new material covering transformer architecture in the context of time-series anomaly detection. After completing this book, you will have a thorough understanding of anomaly detection as well as an assortment of methods to approach it in various contexts, including time-series data. Additionally, you will have gained an introduction to scikit-learn, GANs, transformers, Keras, and PyTorch, empowering you to create your own machine learning- or deep learning-based anomaly detectors. What You Will LearnUnderstand what anomaly detection is, why it it is important, and how it is appliedGrasp the core concepts of machine learning. Master traditional machine learning approaches to anomaly detection using scikit-kearn. Understand deep learning in Python using Keras and PyTorchProcess data through pandas and evaluate your model's performance using metrics like F1-score, precision, and recallApply deep learning to supervised, semi-supervised, and unsupervised anomaly detection tasks for tabular datasets and time series applications Who This Book Is ForData scientists and machine learning engineers of all levels of experience interested in learning the basics of deep learning applications in anomaly detection.

DKK 476.00
1

Automated Pattern Recognition of Communication Behaviour in Electronic Business Negotiations - Muhammed Fatih Kaya - Bog - Springer-Verlag Berlin and

Automated Pattern Recognition of Communication Behaviour in Electronic Business Negotiations - Muhammed Fatih Kaya - Bog - Springer-Verlag Berlin and

The world of digitalisation is changing the way how people and business companies communicate with each other. Electronic negotiations represent one of the most important forms of business communication and can influence the successes and failures of companies in a significant way, whether in interorganisational or intraorganisational processes. Analysing negotiation interactions to determine pattern-based peculiarities in the communication offers new value-adding information concerning the management of optimised communication processes, even though the machine-based processing of communication data bears a series of challenges. The present book develops a new approach to analyse the automated pattern recognition potential of Machine Learning methods in unstructured negotiation communication. It presents holistic research frameworks for the effective detection of structural patterns and reveals the pattern labelling potential in high-dimensional communication data by analytically implementing a series of Machine Learning methods.

DKK 717.00
1

Secure Systems Development with UML - Jan Jurjens - Bog - Springer-Verlag Berlin and Heidelberg GmbH & Co. KG - Plusbog.dk

Secure Systems Development with UML - Jan Jurjens - Bog - Springer-Verlag Berlin and Heidelberg GmbH & Co. KG - Plusbog.dk

Attacks against computer systems can cause considerable economic or physical damage. High-quality development of security-critical systems is difficult, mainly because of the conflict between development costs and verifiable correctness. Jürjens presents the UML extension UMLsec for secure systems development. It uses the standard UML extension mechanisms, and can be employed to evaluate UML specifications for vulnerabilities using a formal semantics of a simplified fragment of UML. Established rules of security engineering can be encapsulated and hence made available even to developers who are not specialists in security. As one example, Jürjens uncovers a flaw in the Common Electronic Purse Specification, and proposes and verifies a correction. With a clear separation between the general description of his approach and its mathematical foundations, the book is ideally suited both for researchers and graduate students in UML or formal methods and security, and for advanced professionals writing critical applications.

DKK 583.00
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Mean Oscillations and Equimeasurable Rearrangements of Functions - Anatolii A. Korenovskii - Bog - Springer-Verlag Berlin and Heidelberg GmbH & Co. KG

Mean Oscillations and Equimeasurable Rearrangements of Functions - Anatolii A. Korenovskii - Bog - Springer-Verlag Berlin and Heidelberg GmbH & Co. KG

This volume considers various applications of equimeasurable function rearrangements to the "best constant"-type problems. It presents several classical theorems along with some very recent results. Coverage includes a product-space extension of the Rising Sun lemma, a product-space version of the John-Nirenberg inequality for bounded mean oscillation functions with sharp exponent, and sharp embedding theorems for Muckenhoupt, Gurov-Reshetnyak, and Gehring classes.

DKK 324.00
1

Unleashing The Power of ChatGPT - Charles Waghmare - Bog - Springer-Verlag Berlin and Heidelberg GmbH & Co. KG - Plusbog.dk

Unleashing The Power of ChatGPT - Charles Waghmare - Bog - Springer-Verlag Berlin and Heidelberg GmbH & Co. KG - Plusbog.dk

Explore the role ChatGPT can play in business, including operations, marketing, sales, and delivery. This concise book illustrates how ChatGPT is changing the way individuals interact with machines and how you can take advantage of its capabilities for business. The book starts with an overview of ChatGPT and its impact in the realm of of conversational AI. You will then dive into the technical aspects of ChatGPT and gain an understanding of how machine learning algorithms and natural language processing work in the background. Various business applications of ChatGPT are then discussed, followed by how it can get integrated into your business operations. To wrap things up, you will gain insight into the data and privacy elements that need to be considered while using ChatGPT, and how to maintain its integrity. After completing this book, you will understand the ChatGPT framework and how to integrate it into your own ventures. What You Will LearnUnderstand the various technologies and techniques utilized in ChatGPTGain insight into the future of human-machine interactionAnalyze the advantages and disadvantages of ChatGPT for your industryExplore the ethical implications of using AI Who This Book Is ForTech enthusiasts who are passionate about artificial intelligence (AI), machine learning, and human-machine interaction.

DKK 264.00
1

AWS IoT With Edge ML and Cybersecurity - Syed Rehan - Bog - Springer-Verlag Berlin and Heidelberg GmbH & Co. KG - Plusbog.dk

AWS IoT With Edge ML and Cybersecurity - Syed Rehan - Bog - Springer-Verlag Berlin and Heidelberg GmbH & Co. KG - Plusbog.dk

In an era where the fusion of connectivity and technology is redefining industries, this book is a must-have guide for aspiring technologists and seasoned professionals alike. This meticulously crafted handbook guides you through every aspect of AWS IoT, touching upon vital spheres such as edge computing, machine learning, and industrial IoT, with a distinct spotlight on cybersecurity. Over the course of this book, industry veteran Syed Rehan unveils the secrets to setting up your AWS IoT environment and walks you through advanced concepts such as leveraging MQTT, mastering Digital Twin implementation, and plumbing the depths of edge machine learning. Whether you're experimenting with virtual devices or hands-on with Raspberry Pi setups, this book will steer you towards innovation and a transformative journey where technology meets intelligence and security, all under the expansive umbrella of AWS IoT. What You Will LearnAWS Mastery: Dive deep into AWS IoT essentials, from setup to hands-on SDK toolsAdvanced Connectivity: Explore advanced MQTT features and the potential of AWS IoT Core MQTT clientsDevice Integration: Master AWS IoT device creation, connection, and deployment, blending the digital and physicalDigital Twin: Unleash IoT's future with AWS IoT device shadow, capitalizing on digital twin technologyDevice Management: Transform remote oversight using secure IoT tunneling and innovative actionsEdge Development: Merge IoT and Machine Learning via AWS IoT Greengrass, spotlighting image classificationData to Insights: Swiftly move from raw data to actionable insights leveraging Amazon TimestreamIoT Cybersecurity: Strengthen your defenses using AWS IoT Device Defender, Zero Trust principles, and Machine Learning (ML) Detect to prepare for and counter threatsWho This Book Is ForIoT developers and enthusiasts, professionals who want to implement IoT solutions in industrial scenarios, system architects and designers interested in edge machine learning, business intelligence analysts, and cybersecurity professionals.

DKK 476.00
1

Energy versus Carbon Dioxide - Cornel Stan - Bog - Springer-Verlag Berlin and Heidelberg GmbH & Co. KG - Plusbog.dk

Energy versus Carbon Dioxide - Cornel Stan - Bog - Springer-Verlag Berlin and Heidelberg GmbH & Co. KG - Plusbog.dk

​This book is focussed on forms of energy for the future, while maintaining climate neutrality, partly by drastically reducing, partly by recycling the resulting carbon dioxide emissions. Electric drive of cars and machines instead of combustion engines do not solve the conflict between energy and carbon dioxide, more efficient ways are described. The book presents hopeful forms of energy conversion without carbon dioxide such as photovoltaics, wind power and hydropower, with their advantages, but also with their disadvantages. More promissing is the energy generation maintaining climate neutrality: The water cycle nature-electrolysis-machine-nature is compared with the carbon dioxide cycle nature- photosynthesis in plant-machine-nature. The results of this analysis are largely surprising from such perspective.

DKK 264.00
1

Statistical Quantitative Methods in Finance - Samit Ahlawat - Bog - Springer-Verlag Berlin and Heidelberg GmbH & Co. KG - Plusbog.dk

Statistical Quantitative Methods in Finance - Samit Ahlawat - Bog - Springer-Verlag Berlin and Heidelberg GmbH & Co. KG - Plusbog.dk

Statistical quantitative methods are vital for financial valuation models and benchmarking machine learning models in finance. This book explores the theoretical foundations of statistical models, from ordinary least squares (OLS) to the generalized method of moments (GMM) used in econometrics. It enriches your understanding through practical examples drawn from applied finance, demonstrating the real-world applications of these concepts. Additionally, the book delves into non-linear methods and Bayesian approaches, which are becoming increasingly popular among practitioners thanks to advancements in computational resources. By mastering these topics, you will be equipped to build foundational models crucial for applied data science, a skill highly sought after by software engineering and asset management firms. The book also offers valuable insights into quantitative portfolio management, showcasing how traditional data science tools can be enhanced with machine learning models. These enhancements are illustrated through real-world examples from finance and econometrics, accompanied by Python code. This practical approach ensures that you can apply what you learn, gaining proficiency in the statsmodels library and becoming adept at designing, implementing, and calibrating your models. By understanding and applying these statistical models, you enhance your data science skills and effectively tackle financial challenges. What You Will LearnUnderstand the fundamentals of linear regression and its applications in financial data analysis and predictionApply generalized linear models for handling various types of data distributions and enhancing model flexibilityGain insights into regime switching models to capture different market conditions and improve financial forecastingBenchmark machine learning models against traditional statistical methods to ensure robustness and reliability in financial applications Who This Book Is ForData scientists, machine learning engineers, finance professionals, and software engineers

DKK 391.00
1

Conjoint Measurement - - Bog - Springer-Verlag Berlin and Heidelberg GmbH & Co. KG - Plusbog.dk

Conjoint Measurement - - Bog - Springer-Verlag Berlin and Heidelberg GmbH & Co. KG - Plusbog.dk

The book covers all recent developments in Conjoint Analysis. Leading scientists present theory and applications of this technique. In short, the following models, techniques, and applications are discussed: normative models that maximize return, extension of choice-based conjoint simulations, latent class, hierarchical Bayes modelling, new choice simulators, normative models for representing competitive actions and reactions (based on game theory), applications in diverse areas, computation of monetary equivalents of part worth, share/return optimisation (including Pareto frontier analysis), coupling of conjoint analysis with the perceptual and preference mapping of choice simulator results.

DKK 731.00
1

Airline Revenue Management - Curt Cramer - Bog - Springer-Verlag Berlin and Heidelberg GmbH & Co. KG - Plusbog.dk

Airline Revenue Management - Curt Cramer - Bog - Springer-Verlag Berlin and Heidelberg GmbH & Co. KG - Plusbog.dk

The book provides a comprehensive overview of current practices and future directions in airline revenue management. It explains state-of-the-art revenue management approaches and outlines how these will be augmented and enhanced through modern data science and machine learning methods in the future. Several practical examples and applications will make the reader familiar with the relevance of the corresponding ideas and concepts for an airline commercial organization. The book is ideal for both students in the field of airline and tourism management as well as for practitioners and industry experts seeking to refresh their knowledge about current and future revenue management approaches, as well as to get an introductory understanding of data science and machine learning methods. Each chapter closes with a checkpoint, allowing the reader to deepen the understanding of the contents covered. This textbook has been recommended and developed for university courses in Germany, Austria and Switzerland.

DKK 281.00
1