Автор: James, Gareth Witten, Daniela Hastie, Trevor Tibsh Название: Introduction to statistical learning ISBN: 1071614177 ISBN-13(EAN): 9781071614174 Издательство: Springer Рейтинг: Цена: 8384.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more.
Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers.
An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.
This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naive Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.
Автор: Huntington-Klein Nick Название: The Effect: An Introduction to Research Design and Causality ISBN: 1032127457 ISBN-13(EAN): 9781032127453 Издательство: Taylor&Francis Рейтинг: Цена: 13014.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The Effect: An Introduction to Research Design and Causality is about research design, specifically concerning research that uses observational data to make a causal inference. It is separated into two halves, each with different approaches to that subject.
Автор: Timbers, Tiffany-anne (university Of British Colum Название: Data science ISBN: 0367524686 ISBN-13(EAN): 9780367524685 Издательство: Taylor&Francis Рейтинг: Цена: 7501.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Data Science: An Introduction focuses on using the R programming language in Jupyter notebooks to perform basic data manipulation and cleaning, create effective visualizations, and extract insights from data using supervised predictive models.
Автор: Giuseppe Da Prato Название: An Introduction to Infinite-Dimensional Analysis ISBN: 3642421687 ISBN-13(EAN): 9783642421686 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Based on well-known lectures given at Scuola Normale Superiore in Pisa, this book introduces analysis in a separable Hilbert space of infinite dimension. It starts from the definition of Gaussian measures in Hilbert spaces, concepts such as the Cameron-Martin formula, Brownian motion and Wiener integral are introduced in a simple way.
Автор: Maurizio Petrelli Название: Introduction to Python in Earth Science Data Analysis ISBN: 3030780546 ISBN-13(EAN): 9783030780548 Издательство: Springer Рейтинг: Цена: 7622.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This textbook introduces the use of Python programming for exploring and modelling data in the field of Earth Sciences.
Автор: Huntington-Klein Nick Название: The Effect: An Introduction to Research Design and Causality ISBN: 1032125780 ISBN-13(EAN): 9781032125787 Издательство: Taylor&Francis Рейтинг: Цена: 4898.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The Effect: An Introduction to Research Design and Causality is about research design, specifically concerning research that uses observational data to make a causal inference. It is separated into two halves, each with different approaches to that subject.
Описание: This book brings together, in a single volume, the fields of multicriteria decision making and multiobjective optimization that are traditionally covered by different books. It is written in a didactic form using examples to help understanding of the proposed methodologies better.
Описание: This book provides the theoretical background to approach decision theory from a statistical perspective. It covers both traditional approaches, in terms of value theory and expected utility theory, and recent developments, in terms of causal inference.
Автор: Jebril, Iqbal H., Название: Concise introduction to logic and set theory / ISBN: 0367077957 ISBN-13(EAN): 9780367077952 Издательство: Taylor&Francis Рейтинг: Цена: 24499.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book deals with two most important branches of mathematics, namely, logic and set theory. Logic and set theory are two closely related branches of mathematics that play very crucial roles in the foundations of mathematics, and together produce several results in all of mathematics.
Автор: Kolassa, John E. Название: An Introduction to nonparametric statistics ISBN: 0367194848 ISBN-13(EAN): 9780367194840 Издательство: Taylor&Francis Рейтинг: Цена: 14086.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents the theory and practice of non-parametric statistics, with an emphasis on motivating principals. The course is a combination of traditional rank based methods and more computationally-intensive topics like density estimation, kernel smoothers in regression, and robustness. The text is aimed at MS students.
Описание: This class-tested textbook will provide in-depth coverage of the fundamentals of machine learning, with an exploration of applications in information security. The book will cover malware detection, cryptography, and intrusion detection. The book will be relevant for students in machine learning and computer security courses.
Описание: Offers a cohesive framework for statistical modeling. Emphasizing numerical and graphical methods, this work enables readers to understand the unifying structure that underpins GLMs. It discusses common concepts and principles of advanced GLMs, including nominal and ordinal regression, survival analysis, and longitudinal analysis.
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