Inference and Learning from Data: Volume 1: Foundations, Ali H. Sayed
Автор: Bradley Efron , Trevor Hastie Название: Computer Age Statistical Inference, Student Edition ISBN: 1108823416 ISBN-13(EAN): 9781108823418 Издательство: Cambridge Academ Рейтинг: Цена: 5069.00 р. 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: Mohri Mehryar, Rostamizadeh Afshin, Talwalkar Ameet Название: Foundations of Machine Learning, 2 ed. ISBN: 0262039400 ISBN-13(EAN): 9780262039406 Издательство: MIT Press Рейтинг: Цена: 12697.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms.
This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics.
Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVM); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes aoffer dditional material including concise probability review.
This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition.
Описание: There is currently no single book that covers the mathematics, circuits, and electromagnetics backgrounds needed for the study of electromagnetic compatibility (EMC). This book aims to redress the balance by focusing on EMC and providing the background in all three disciplines.
Автор: Alfred Olivier Hero; David Casta??n; Doug Cochran; Название: Foundations and Applications of Sensor Management ISBN: 1441939113 ISBN-13(EAN): 9781441939111 Издательство: Springer Рейтинг: Цена: 26120.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book covers control theory signal processing and relevant applications in a unified manner. It introduces the area, takes stock of advances, and describes open problems and challenges in order to advance the field.
Автор: Gilles Barthe, Joost-Pieter Katoen, Alexandra Silva Название: Foundations of probabilistic programming ISBN: 110848851X ISBN-13(EAN): 9781108488518 Издательство: Cambridge Academ Рейтинг: Цена: 9187.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides an overview of the theoretical underpinnings of modern probabilistic programming and presents applications in e.g., machine learning, security, and approximate computing. Comprehensive survey chapters make the material accessible to graduate students and non-experts. This title is also available as Open Access on Cambridge Core.
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: 13974.00 р. 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: 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: 16769.00 р. 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: Peters Jonas, Janzing Dominik, Scholkopf Bernhard Название: Elements of Causal Inference: Foundations and Learning Algorithms ISBN: 0262037319 ISBN-13(EAN): 9780262037310 Издательство: MIT Press Рейтинг: Цена: 7719.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
A concise and self-contained introduction to causal inference, increasingly important in data science and machine learning.
The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduction to causal models and how to learn them from data.
After explaining the need for causal models and discussing some of the principles underlying causal inference, the book teaches readers how to use causal models: how to compute intervention distributions, how to infer causal models from observational and interventional data, and how causal ideas could be exploited for classical machine learning problems. All of these topics are discussed first in terms of two variables and then in the more general multivariate case. The bivariate case turns out to be a particularly hard problem for causal learning because there are no conditional independences as used by classical methods for solving multivariate cases. The authors consider analyzing statistical asymmetries between cause and effect to be highly instructive, and they report on their decade of intensive research into this problem.
The book is accessible to readers with a background in machine learning or statistics, and can be used in graduate courses or as a reference for researchers. The text includes code snippets that can be copied and pasted, exercises, and an appendix with a summary of the most important technical concepts.
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: Blum Avrim Название: Foundations of Data Science ISBN: 1108485065 ISBN-13(EAN): 9781108485067 Издательство: Cambridge Academ Рейтинг: Цена: 7445.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book is aimed towards both undergraduate and graduate courses in computer science on the design and analysis of algorithms for data. The material in this book will provide students with the mathematical background they need for further study and research in machine learning, data mining, and data science more generally.
Автор: Homenda Wladyslaw Название: Automata Theory and Formal Languages ISBN: 3110752271 ISBN-13(EAN): 9783110752274 Издательство: Walter de Gruyter Рейтинг: Цена: 9930.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
The book is a concise, self-contained and fully updated introduction to automata theory - a fundamental topic of computer sciences and engineering. The material is presented in a rigorous yet convincing way and is supplied with a wealth of examples, exercises and down-to-the earth convincing explanatory notes. An ideal text to a spectrum of one-term courses in computer sciences, both at the senior undergraduate and graduate students.
Автор: Ilan Shomorony, Reinhard Heckel Название: Information-Theoretic Foundations of DNA Data Storage ISBN: 168083956X ISBN-13(EAN): 9781680839562 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 12335.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Discusses the fundamental limits of storing information on DNA. Motivated by current technological constraints on DNA synthesis and sequencing, the authors propose a probabilistic channel model that captures three key distinctive aspects of the DNA storage systems.
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