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Rosenberg S: Practical AI Governance

Practical AI Governance

Building a Program for Strategy and Oversight

Expert reference on building surrogate models, optimization using them, prediction uncertainty associate with them, and their potential failure, with practical implementation in MATLAB Surrogate Modeling and Optimization explains the meaning of different surrogate models and provides an in-depth understanding of such surrogates, emphasizing how much uncertainty is associated with them, and when and how a surrogate model can fail in approximating complex functions and helping readers understand theory through practical implementation in MATLAB. This book enables readers to obtain an accurate approximate function using as few samples as possible, thereby allowing them to replace expensive computer simulations and experiments during design optimization, sensitivity analysis, and/or uncertainty quantification. The book is organized into three parts. Part I introduces the basics of surrogate modeling. Part II reviews various theories and algorithms of design optimization. Part III presents advanced topics in surrogate modeling, including the Kriging surrogate, neural network models, multi-fidelity surrogates, and efficient global optimization using Kriging surrogates. The book is divided into 10 chapters. Each chapter contains about 10 examples and 20 exercise problems. Lecture slides and a solution manual for exercise problems are available for instructors on a companion website. Sample topics discussed in Surrogate Modeling and Optimization include: <ul> <li>Various designs of experiments, such as those developed for linear and quadratic polynomial response surfaces (PRS) in a boxlike design space</li> <li>Criteria for constrained and unconstrained optimization and the most important optimization theories</li> <li>Various numerical algorithms for gradient-based optimization</li> <li>Gradient-free optimization algorithms, often referred to as global search algorithms, which do not require gradient or Hessian information</li> <li>Detailed explanations and implementation on Kriging surrogate, often referred to as Gaussian Process, especially when samples include noise</li> <li>The combination of a small number of high-fidelity samples with many low-fidelity samples to improve prediction accuracy</li> <li>Neural network models, focusing on training uncertainty and its effect on prediction uncertainty</li> <li>Efficient global optimization using either polynomial response surfaces or Kriging surrofates</li> </ul> Surrogate Modeling and Optimization is an essential learning companion for senior-level undergraduate and graduate students in all engineering disciplines, including mechanical, aerospace, civil, biomedical, and electrical engineering. The book is also valuable for industrial practitioners who apply the surrogate model to solve their optimization problems.

Theories, Applications, and Limitations

Surrogate Modeling and Optimization

Modern Trends In Financial Engineering

Multiscale Financial Data Analytics and Machine Learning offers a systematic and comprehensive study on the multiscale approach to financial data analytics and machine learning. This book covers an array of multiscale methods to discover the properties of various timescales embedded in a financial time series, including noise-assisted empirical mode decomposition methods. Important interpretable multiscale outputs from the estimation are recognized as a new set of features that can be used for machine learning. The feature selection problem for machine learning is examined in this volume.This book offers an applied quantitative approach that combines novel analytical methodologies and practical applications to a wide array of examples with real-world data. It is self-contained and organized in its presentation. The explanations of the methodologies are both accessible and detailed enough to capture the interest of the curious student or researcher. Step-by-step descriptions of the algorithms are provided for straightforward implementation.

Multiscale Financial Data Analytics And Machine Learning

Foundations, Technologies, and Prospects for the Future

Autonomous Vehicle Safety Solutions

Sustainable Computing and Optimization

Trends and Future Challenges

Managing Sustainable Operations of Logistics and Supply Chain Management

Synthesis Lectures on Technology and Health

<div>This book provides a comprehensive overview of palliative care digital health by covering current developments, methodology, clinical relevance, research priorities, ethical issues, and policy related to technological innovations for those with serious illness. This type of book does not currently exist in the literature, as previous work primarily focuses on digital health for early identification of disease and facilitating self-care or ‘aging in place’ practices. This book addresses the lack of focus on digital health for people with palliative care needs. This book has a special focus on the needs of individuals as their health worsens throughout their illness. Consequently, the reader will benefit from this book’s focus on the science, methodology, application, and practicality of digital health developments in palliative care.</div>
<div>This book explores the barriers that prevent the meaningful use of digital health in palliative care, including expenses, interoperability issues, data privacy and security concerns, lack of effectiveness and equity, and the concern that technology may reduce face-to-face consultations between patients and clinicians. There is an overall lack of consensus about digital health research priorities in palliative care, and a lack of theories about how these technologies might improve care outcomes. Therefore, this book emphasizes the importance of identifying, discussing, and debating how technological innovations can be meaningfully used to ensure that frail people have appropriate access to palliative care services. Consequently, this book begins the conversation about articulating digital health research priorities in palliative care to determine how finite resources should be allocated to a field saturated with rapidly developing innovation.</div>

Technologies in Palliative Care for Older People

Vital information on AI, ML, and 5G, and implementing these topics for cellular network design and optimization AI for Design and Optimization of Cellular Networks focuses on the intersection of cellular communications and AI, discussing current advances, key concepts, challenges, opportunities, and lifecycle management. Divided into two parts, the first part provides the background of AI and cellular communications, exploring the challenges that are associated with creation, deployment, operation, and maintenance of systems for the design and optimization of cellular networks. The second part explains the process of network planning, design, and optimization of cellular networks, including examples and use cases in each area. The second part also discusses the application of AI to energy saving, the challenges that operators face, and a glimpse of future trends for AI, cellular networks, and their related NDO (Network Design and Optimization) activities. The book also includes coverage of AI-powered design technologies, performance characterization of advanced antenna systems, propagation predictions, and more. With heavy contribution from industry professionals at Ericsson, AI for Design and Optimization of Cellular Networks includes detailed information on: Evolution of cellular communications from the early GSM days until today, with special emphasis on the arrival of 5G and its implications compared with LTESupervised, unsupervised, semi-supervised, and reinforcement learning with an intuitive focus, covering their distinctive characteristicsDifferent types of digital twins, their fundamental properties, their suitability for design and optimization of cellular networks, and using them together with AI techniquesTransition from traditional network performance management and operations towards an AI-driven combination of control loops An informed and timely exploration of key developments and challenges by those who work on the frontline, AI for Design and Optimization of Cellular Networks is an essential resource for professionals working in cellular communications and AI, providing a one-of-a-kind blend of practical and theoretical information.

AI for Design and Optimization of Cellular Networks

Applications in R and Python

Essentials of Big Data Analytics

Driving Business Success and Profit Through Enterprise Automation and AI Agents

Untangling AI

Understanding and preventing financial misconduct

The Fault Lines of Finance

When you think about the tools at our disposal to address the ecological, social and biodiversity challenges we face today, accounting might not immediately come to mind. And yet it is accounting standards, not science and not public policy, that set out how profit is calculated, which in turn drives investment decisions. What if we demanded that those standards reflect the true cost of business and its consequences?Unless we look again at these fundamental principles, which have gone under the radar for so long, capital markets will continue to contribute to inequality, nature loss and climate change. What's remarkable is that we already have in place everything we need for a more just and complete approach to accounting, and forward-thinking businesses are beginning to recognize that.Jeremy Nicholls - accountant, sustainability professional, historian and activist - sets out a vision for new way of accounting, and it's one that we can adopt immediately.

How financial accounting is damaging the world (but can help repair it)

The Accounting Paradox

How to Add Business Value as a People Professional

High-Impact HR

Practical AI Governance

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