Mathematical analysis of machine learning algorithms, Zhang, Tong (hong Kong University Of Science And Technology)
Автор: Cormen, Thomas H. Название: Introduction to Algorithms 4E ISBN: 026204630X ISBN-13(EAN): 9780262046305 Издательство: Random House (USA) Рейтинг: Цена: 13794.00 р. Наличие на складе: Ожидается поступление.
Описание: A comprehensive update of a widely used textbook, with new material on matchings in bipartite graphs, online algorithms, machine learning, and other topics. Some books on algorithms are rigorous but incomplete; others cover masses of material but lack rigor. Introduction to Algorithms uniquely combines rigor and comprehensiveness. It covers a broad range of algorithms in depth, yet makes their design and analysis accessible to all levels of readers. Since the publication of the first edition, Introduction to Algorithms has become a widely used text in universities worldwide as well as the standard reference for professionals. This fourth edition has been updated throughout, with new chapters on matchings in bipartite graphs, online algorithms, and machine learning, and new material on such topics as solving recurrence equations, hash tables, potential functions, and suffix arrays. Each chapter is relatively self-contained, presenting an algorithm, a design technique, an application area, or a related topic, and can be used as a unit of study. The algorithms are described in English and in a pseudocode designed to be readable by anyone who has done a little programming. The explanations have been kept elementary without sacrificing depth of coverage or mathematical rigor. The fourth edition has 140 new exercises and 22 new problems, and color has been added to improve visual presentations. The writing has been revised throughout, and made clearer, more personal, and gender neutral. The book's website offers supplemental material.
Автор: Daniel J. Velleman Название: How to Prove It : A Structured Approach ISBN: 1108439535 ISBN-13(EAN): 9781108439534 Издательство: Cambridge Academ Рейтинг: Цена: 5861.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Proofs play a central role in advanced mathematics and theoretical computer science, and this bestselling text`s third edition will help students transition from solving problems to proving theorems by teaching them the techniques needed to read and write proofs, with a new chapter on number theory and over 150 new exercises.
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: Brunton, Steven L. (university Of Washington) Kutz Название: Data-driven science and engineering ISBN: 1009098489 ISBN-13(EAN): 9781009098489 Издательство: Cambridge Academ Рейтинг: Цена: 7918.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Data-driven discovery is revolutionizing how we model, predict, and control complex systems. This text integrates emerging machine learning and data science methods for engineering and science communities. Now with Python and MATLAB (R), new chapters on reinforcement learning and physics-informed machine learning, and supplementary videos and code.
Автор: Garban Название: Noise Sensitivity of Boolean Functions and Percolation ISBN: 1107432553 ISBN-13(EAN): 9781107432550 Издательство: Cambridge Academ Рейтинг: Цена: 6019.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This account of the new and exciting area of noise sensitivity of Boolean functions - in particular applied to critical percolation - is designed for graduate students and researchers in probability theory, discrete mathematics, and theoretical computer science. It assumes a basic background in probability theory and integration theory. Each chapter ends with exercises.
Автор: G?nter Mayer Название: Interval Analysis: and Automatic Result Verification ISBN: 3110500639 ISBN-13(EAN): 9783110500639 Издательство: Walter de Gruyter Рейтинг: Цена: 22305.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
This self-contained text is a step-by-step introduction and a complete overview of interval computation and result verification, a subject whose importance has steadily increased over the past many years. The author, an expert in the field, gently presents the theory of interval analysis through many examples and exercises, and guides the reader from the basics of the theory to current research topics in the mathematics of computation.
Описание: Partial differential equations are one of the most used widely forms of mathematics in science and engineering. Two fractional PDEs can be considered, fractional in time, and fractional in space. This volume is directed to the development and use of SFPDEs, providing a discussion of applications from classical integer PDEs.
Описание: Partial differential equations are one of the most used widely forms of mathematics in science and engineering. Two fractional PDEs can be considered, fractional in time, and fractional in space. These two volumes are directed to the development and use of SFPDEs, with the discussion divided into an introduction to Algorithms and Computer Coding in R and applications from classical integer PDEs.
Автор: Fred J. Hickernell, Peter Kritzer Название: Multivariate Algorithms and Information-Based Complexity ISBN: 3110633116 ISBN-13(EAN): 9783110633115 Издательство: Walter de Gruyter Цена: 19330.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
The series is devoted to the publication of high-level monographs, surveys and proceedings which cover the whole spectrum of computational and applied mathematics.
The books of this series are addressed to both specialists and advanced students.
Interested authors may submit book proposals to the Managing Editor or to any member of the Editorial Board.
Managing Editor Ulrich Langer, RICAM, Linz, Austria; Johannes Kepler University Linz, Austria
Автор: Tim Roughgarden Название: Beyond the Worst-Case Analysis of Algorithms ISBN: 1108494315 ISBN-13(EAN): 9781108494311 Издательство: Cambridge Academ Рейтинг: Цена: 9187.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Understanding when and why algorithms work is a fundamental challenge. For problems ranging from clustering to linear programming to neural networks there are significant gaps between empirical performance and prediction based on traditional worst-case analysis. The book introduces exciting new methods for assessing algorithm performance.
Автор: Graham Cormode, Ke Yi Название: Small Summaries for Big Data ISBN: 1108477445 ISBN-13(EAN): 9781108477444 Издательство: Cambridge Academ Рейтинг: Цена: 7602.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The massive volume of data generated in modern applications requires the ability to build compact summaries of datasets. This introduction aimed at students and practitioners covers algorithms to describe massive data sets from simple sums to advanced probabilistic structures, with applications in big data, data science, and machine learning.
Автор: Lange, Kenneth Название: Algorithms from the book ISBN: 1611976162 ISBN-13(EAN): 9781611976168 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 8966.00 р. Наличие на складе: Поставка под заказ.
Описание: Algorithms are a dominant force in modern culture, and every indication is that they will become more pervasive, not less. The best algorithms are undergirded by beautiful mathematics.This text cuts across discipline boundaries to highlight some of the most famous and successful algorithms. Readers are exposed to the principles behind these examples and guided in assembling complex algorithms from simpler building blocks.Algorithms from THE BOOK:Incorporates Julia code for easy experimentation.Is written in clear, concise prose consistent with mathematical rigour.Includes a large number of classroom-tested exercises at the end of each chapter.Covers background material, often omitted from undergraduate courses, in the appendices.This textbook is aimed at first-year graduate and advanced undergraduate students. It will also serve as a convenient reference for professionals throughout the mathematical sciences, physical sciences, engineering, and the quantitative sectors of the biological and social sciences.
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