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James V Lambers: Explorations In
Numerical Analysis And Machine Learning

Explorations In Numerical Analysis And Machine Learning With Julia

Concepts, Models, and Applications

Learning-Driven Game Theory for AI

This second edition of Linear Operator Equations: Approximation and Regularization provides a modern, accessible, and student-friendly introduction to the theory and numerical treatment of linear operator equations, both well-posed and ill-posed.With rewritten and expanded proofs, a more consistent structure, and numerous new exercises and examples, the book offers clear pathways for learning approximation theory, regularization techniques, and applications of functional analysis to inverse problems. New topics include truncated singular value decomposition, generalized Tikhonov regularization with stabilizing operators, and a deeper treatment of discrete projection methods for operator equations.Designed for students and researchers alike, the book balances theoretical rigor with practical insights, making it a valuable resource for courses in numerical functional analysis, inverse problems, and operator theory, or for self-study by applied mathematicians, engineers, and computational scientists.A newly expanded bibliography, revised notation, and systematic numbering make this edition even more usable as a reference and teaching tool.

Well-posed And Ill-posed Equations: Approximation And Regularization

Encyclopedia of Optimization

A comprehensive, up-to-date introduction to active inference and the free energy principle for an engineering-focused audience. Active inference, which uses machine learning to model brain function and behavior, emerged from decades of cross-disciplinary research in computational neuroscience, resulting in a vast literature but no unifying treatment. Filling this gap, Sanjeev Namjoshi provides comprehensive coverage of the foundational material needed to understand and navigate this fast-moving field from first principles. Using a simple, conversational style free of proofs, lemmas, and theorems, Namjoshi brings together theory and technical material in one self-contained text. The book begins with an explanation of the general statistical framework used in active inference models that describes the relationship between artificial agents and their environments. It then introduces fundamental concepts in machine learning and statistics and connects them to the active inference perspective. Featuring worked examples, simulations, and detailed walkthroughs of concepts, this user-friendly text aims to expand the readership of active inference to an engineering-focused audience.&nbsp; <ul><li>Provides a one-stop-shop for understanding the foundations and applications of active inference and the free energy principle </li><li>Makes a complex, interdisciplinary subject accessible to students and professionals beyond the neurosciences</li><li>Covers discrete and continuous state-space formulations of active inference as well as state-of-the-art extensions to base active inference methods</li><li>Features extensive appendices and supplemental resources</li></ul>

Principles, Algorithms, and Applications of the Free Energy Principle for Engineers

Fundamentals of Active Inference

This unique compendium develops a general approach to building models of economic and financial processes, with a focus on statistical learning techniques that scale to large data sets. It introduces the key elements of a parametric statistical model: likelihood, prior, and posterior, and show how to use them to make predictions.The book covers classical techniques such as multiple regression and the Kalman filter in a clear, accessible style that has been popular with students, but also includes detailed treatments of state-of-the-art models, highlighting tree-based methods, support vector machines and kernel methods, deep learning, and reinforcement learning. Theories are supplemented by real-world examples.This reference text is useful for undergraduate, graduate and even PhD students in quantitative finance, and also to practitioners who are facing the reality that data science and machine learning are disrupting the industry.

Statistical Machine Learning With Applications In Finance

'Excellent' - Geoffrey Hinton, the 'godfather of AI' and 2024 Nobel Laureate in Physics 'An indispensable guide to both ourselves and the coming age of AI' - Mustafa Suleyman, bestselling co-author of The Coming Wave How does our consciousness - our ability to think, feel and act - actually work? How is this human intelligence different from artificial intelligence? And as we stand on the brink of a new era of machine intelligence, how are these realms of intelligence beginning to intersect? In The Emergent Mind, leading scientists Gaurav Suri and Jay McClelland offer a groundbreaking new perspective on these urgent questions. By examining the concept of emergence - where complex systems arise from the interaction of simple elements - they reveal how mind-like abilities form in both human intelligence and AI. Drawing on the foundational idea of neural networks, a framework inspired by the human brain and now the bedrock of modern AI, this insightful book offers a clear and accessible path to understanding how intelligence takes shape. The Emergent Mind provides a fascinating account of how we reach decisions, why we change our minds, and how we are affected by context and experience. they are showing how a data-driven neural network can create thoughts, emotions and ideas - a mind - whether in humans or machines. Written by experts at the forefront of cognitive science and AI, The Emergent Mind is an essential read for anyone captivated by the mysteries of human intelligence or the transformative rise of AI. 'Fascinating' - Lisa Feldman Barrett, author of How Emotions Are Made 'Deeply stimulating and disarmingly accessible' - Robert Sapolsky, author of Determined 'Lucid, invaluable' - Steven Pinker, author of Rationality

How Intelligence Arises in People and Machines

The Emergent Mind

Key Technologies on New Energy Vehicles

This book focuses on the current advanced technology field of battery operation big data and comprehensively describes the frontier technology of big data analysis and intelligent control of battery safe operation. The content systematically introduces the coupling mechanism of battery system safety and consistency, battery SOH estimation and prediction, battery safety state prediction, battery fault diagnosis, battery system risk pre-warning, and safety control strategy research.This book is suitable for teachers and students of universities and colleges in the automotive and related industries, engineers and technicians in the R&amp;D departments of enterprises or automotive enthusiasts to read and refer.The basis of English translation of this book, originally in Chinese, was facilitated by artificial intelligence. The content was later revised by the author for accuracy.

Big Data Analysis and Application of Power Battery Operation Safety

The textbook is an expansion of Explorations in Numerical Analysis that includes new chapters covering topics from machine learning. It is intended for advanced undergraduate and early graduate students, with a focus on the connections between numerical analysis and machine learning.Topics covered include computer arithmetic, error analysis, solution of systems of linear equations by direct and iterative methods, least squares problems, eigenvalue problems, nonlinear equations, optimization, polynomial interpolation and approximation, numerical differentiation and integration, ordinary differential equations, partial differential equations, machine learning, classification, regression, and neural networks.Each problem is presented with derivations of solution techniques, analysis of their efficiency, accuracy and robustness, and detailed implementation using the Julia programming language. This book is suitable for a year-long course in numerical analysis, or for a one-semester course in numerical linear algebra (Part II) or machine learning (Part VI).

How AI Learned to Talk and What It Means

These Strange New Minds

Data Science and Interactive Visualization Tools for the Analysis of Qualitative Evidence empowers qualitative and mixed methods researchers in the data science movement by offering no-code, cost-free software access so that they can apply cutting-edge and innovative methods to synthetize qualitative data. The book builds on the idea that qualitative and mixed methods researchers should not have to learn to code to benefit from rigorous open-source, cost-free software that uses artificial intelligence, machine learning, and data visualization tools-just as people do not need to know C++ or TypeScript to benefit from Microsoft Word. The real barrier is the hundreds of R code lines required to apply these concepts to their databases. By removing the coding proficiency hurdle, this book will empower their research endeavors and help them become active members of and contributors to the applied data science community. The book offers a comprehensive explanation of data science and machine learning methodologies, along with access to software application tools to implement these techniques without any coding proficiency. The book addresses the need for innovative tools that enable researchers to tap into the insights that come out of cutting-edge data science tools with absolutely no computer language literacy requirements.

Data Science and Interactive Visualization Tools for the Analysis of Qualitative Evidence

As data is the most important asset for any organization, it is essential to apply analytics to fulfill the sustainability needs of any organization. This book aims to provide a context for data analytics for the sustainability of organizations demonstrating innovation across a few specific subject areas with detailed coverage. It highlights how data analytics enables organizations to create intelligence by exploring opportunities and overcoming challenges, both present and future.Data Science: Semantic Technology, Intelligent Data Analytics, and Sustainability Practice is packed with up-to-date examples and data, ensuring it's both meaningful and practical for a diverse audience. It begins with an introduction to data analytics in the context of sustainable development. Each chapter explores the different aspects of data analytics for sustainability, utilizing a state-of-the-art context that is easy to grasp. Key concepts are presented through clear explanations, making them accessible to all readers. At the end of each chapter, is a summary of the main points, allowing for quick reviews of the essential ideas. It provides practical insights into applying data analytics concepts towards sustainable development, empowering readers to enhance their decision-making abilities, and discusses a strategic approach aligned with the vision for 2030 and beyond while addressing the anticipated challenges.This resource provides support to researchers, scholars, technology practitioners, business analysts, data scientists, and industry professionals worldwide, promoting globalization and enabling real-life application of findings in their organizations.

Semantic Technology, Intelligent Data Analytics, and Sustainability Practices

Data Science

Springer Handbooks

Springer Handbook of Chem- and Bioinformatics

Explorations In Numerical Analysis And Machine Learning With Julia

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