Inference and Learning from Data: Volume 3: Learning, Ali H. Sayed
Автор: Strang Gilbert Название: Linear Algebra and Learning from Data ISBN: 0692196382 ISBN-13(EAN): 9780692196380 Издательство: Cambridge Academ Рейтинг: Цена: 66520.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Linear algebra and the foundations of deep learning, together at last! From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra, comes Linear Algebra and Learning from Data, the first textbook that teaches linear algebra together with deep learning and neural nets. This readable yet rigorous textbook contains a complete course in the linear algebra and related mathematics that students need to know to get to grips with learning from data. Included are: the four fundamental subspaces, singular value decompositions, special matrices, large matrix computation techniques, compressed sensing, probability and statistics, optimization, the architecture of neural nets, stochastic gradient descent and backpropagation.
Автор: Bradley Efron , Trevor Hastie Название: Computer Age Statistical Inference, Student Edition ISBN: 1108823416 ISBN-13(EAN): 9781108823418 Издательство: Cambridge Academ Рейтинг: Цена: 33790.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Computing power has revolutionized the theory and practice of statistical inference. Now in paperback, and fortified with 130 class-tested exercises, this book explains modern statistical thinking from classical theories to state-of-the-art prediction algorithms. Anyone who applies statistical methods to data will value this landmark text.
Автор: Ali H. Sayed Название: Inference and Learning from Data: Volume 2: Inference ISBN: 1009218263 ISBN-13(EAN): 9781009218269 Издательство: Cambridge Academ Рейтинг: Цена: 82370.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Written in an engaging and rigorous style by a world authority in the field, this is an accessible and comprehensive introduction to techniques for inferring unknown variables and quantities. With downloadable Matlab code and solutions for instructors, this is the ideal introduction for students of data science, machine learning and engineering.
Автор: Judea Pearl Название: Probabilistic Reasoning in Intelligent Systems, ISBN: 1558604790 ISBN-13(EAN): 9781558604797 Издательство: Elsevier Science Рейтинг: Цена: 63990.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic.
The author distinguishes syntactic and semantic approaches to uncertainty--and offers techniques, based on belief networks, that provide a mechanism for making semantics-based systems operational. Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation. Application areas include diagnosis, forecasting, image interpretation, multi-sensor fusion, decision support systems, plan recognition, planning, speech recognition--in short, almost every task requiring that conclusions be drawn from uncertain clues and incomplete information.
Probabilistic Reasoning in Intelligent Systems will be of special interest to scholars and researchers in AI, decision theory, statistics, logic, philosophy, cognitive psychology, and the management sciences. Professionals in the areas of knowledge-based systems, operations research, engineering, and statistics will find theoretical and computational tools of immediate practical use. The book can also be used as an excellent text for graduate-level courses in AI, operations research, or applied probability.
Автор: Aditya Vempaty; Bhavya Kailkhura; Pramod K. Varshn Название: Secure Networked Inference with Unreliable Data Sources ISBN: 9811323119 ISBN-13(EAN): 9789811323119 Издательство: Springer Рейтинг: Цена: 93160.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The book presents theory and algorithms for secure networked inference in the presence of Byzantines. It derives fundamental limits of networked inference in the presence of Byzantine data and designs robust strategies to ensure reliable performance for several practical network architectures. In particular, it addresses inference (or learning) processes such as detection, estimation or classification, and parallel, hierarchical, and fully decentralized (peer-to-peer) system architectures. Furthermore, it discusses a number of new directions and heuristics to tackle the problem of design complexity in these practical network architectures for inference.
Автор: Ali H. Sayed Название: Inference and Learning from Data: Volume 1: Foundations ISBN: 1009218123 ISBN-13(EAN): 9781009218122 Издательство: Cambridge Academ Рейтинг: Цена: 92930.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Written in an engaging and rigorous style by a world authority in the field, this is an accessible and comprehensive introduction to core topics in inference and learning. With downloadable Matlab code and solutions for instructors, this is the ideal introduction for students of data science, machine learning, and engineering.
Автор: Aditya Vempaty; Bhavya Kailkhura; Pramod K. Varshn Название: Secure Networked Inference with Unreliable Data Sources ISBN: 9811347654 ISBN-13(EAN): 9789811347658 Издательство: Springer Рейтинг: Цена: 111790.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The book presents theory and algorithms for secure networked inference in the presence of Byzantines. It derives fundamental limits of networked inference in the presence of Byzantine data and designs robust strategies to ensure reliable performance for several practical network architectures. In particular, it addresses inference (or learning) processes such as detection, estimation or classification, and parallel, hierarchical, and fully decentralized (peer-to-peer) system architectures. Furthermore, it discusses a number of new directions and heuristics to tackle the problem of design complexity in these practical network architectures for inference.
Автор: Sayed, Ali H. (ecole Polytechnique Federale De Lausanne) Название: Inference and learning from data ISBN: 1009218107 ISBN-13(EAN): 9781009218108 Издательство: Cambridge Academ Рейтинг: Цена: 221760.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This extraordinary three-volume work provides an accessible, comprehensive introduction to mathematical and statistical techniques for data-driven learning and inference. Ideal for early-career researchers and graduate students across signal processing, machine learning, statistics and data science.
Автор: Olivier Capp?; Eric Moulines; Tobias Ryden Название: Inference in Hidden Markov Models ISBN: 1441923195 ISBN-13(EAN): 9781441923196 Издательство: Springer Рейтинг: Цена: 181670.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book is a comprehensive treatment of inference for hidden Markov models, including both algorithms and statistical theory. The book builds on recent developments, both at the foundational level and the computational level, to present a self-contained view.
Автор: Bernhard Sch?lkopf; Zhiyuan Luo; Vladimir Vovk Название: Empirical Inference ISBN: 3642411355 ISBN-13(EAN): 9783642411359 Издательство: Springer Рейтинг: Цена: 88500.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book celebrates the work of Vladimir Vapnik, developer of the support vector machine, which combines methods from statistical learning and functional analysis to create a new approach to learning problems, and who continues as active as ever in his field.
Автор: Riguzzi Fabrizio Название: Foundations of Probabilistic Logic Programming: Languages, Semantics, Inference and Learning ISBN: 8770220182 ISBN-13(EAN): 9788770220187 Издательство: Taylor&Francis Рейтинг: Цена: 93910.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The integration of logic and probability combines the capability of the first to represent complex relations among entities with the capability of the latter to model uncertainty over attributes and relations. Logic programming provides a Turing complete language based on logic and thus represent an excellent candidate for the integration.Since its birth, the field of Probabilistic Logic Programming has seen a steady increase of activity, with many proposals for languages and algorithms for inference and learning. One of most successful approaches to Probabilistic Logic Programming is the Distribution Semantics, where a probabilistic logic program defines a probability distribution over normal logic programs and the probability of a ground query is then obtained from the joint distribution of the query and the programs. Foundations of Probabilistic Logic Programming aims at providing an overview of the field of Probabilistic Logic Programming, with a special emphasis on languages under the Distribution Semantics. The book presents the main ideas for semantics, inference and learning and highlights connections between the methods.Many examples of the book include a link to a page of the web application http://cplint.eu where the code can be run online.
Автор: Galindez Olascoaga Laura Isabel, Meert Wannes, Verhelst Marian Название: Hardware-Aware Probabilistic Machine Learning Models: Learning, Inference and Use Cases ISBN: 3030740412 ISBN-13(EAN): 9783030740412 Издательство: Springer Цена: 55890.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book proposes probabilistic machine learning models that represent the hardware properties of the device hosting them.
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