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Statistical Machine Learning: A Unified Framewo, Golden..

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Objectstaat
Nieuw: Een nieuw, ongelezen en ongebruikt boek in perfecte staat waarin geen bladzijden ontbreken of ...
Title
Statistical Machine Learning: A Unified Framework (Chapman & Hal
ISBN
9781138484696
EAN
9781138484696
Publication Year
2020
Type
Textbook
Format
Hardcover
Language
English
Publication Name
Statistical Machine Learning: a Unified Framework
Item Height
254mm
Author
Richard Golden
Publisher
Taylor & Francis LTD
Item Width
178mm
Subject
Computer Science, Mathematics
Item Weight
1438g
Number of Pages
506 Pages

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Product Information

The recent rapid growth in the variety and complexity of new machine learning architectures requires the development of improved methods for designing, analyzing, evaluating, and communicating machine learning technologies. Statistical Machine Learning: A Unified Framework provides students, engineers, and scientists with tools from mathematical statistics and nonlinear optimization theory to become experts in the field of machine learning. In particular, the material in this text directly supports the mathematical analysis and design of old, new, and not-yet-invented nonlinear high-dimensional machine learning algorithms. Features: Unified empirical risk minimization framework supports rigorous mathematical analyses of widely used supervised, unsupervised, and reinforcement machine learning algorithms Matrix calculus methods for supporting machine learning analysis and design applications Explicit conditions for ensuring convergence of adaptive, batch, minibatch, MCEM, and MCMC learning algorithms that minimize both unimodal and multimodal objective functions Explicit conditions for characterizing asymptotic properties of M-estimators and model selection criteria such as AIC and BIC in the presence of possible model misspecification This advanced text is suitable for graduate students or highly motivated undergraduate students in statistics, computer science, electrical engineering, and applied mathematics. The text is self-contained and only assumes knowledge of lower-division linear algebra and upper-division probability theory. Students, professional engineers, and multidisciplinary scientists possessing these minimal prerequisites will find this text challenging yet accessible. About the Author: Richard M. Golden (Ph.D., M.S.E.E., B.S.E.E.) is Professor of Cognitive Science and Participating Faculty Member in Electrical Engineering at the University of Texas at Dallas. Dr. Golden has published articles and given talks at scientific conferences on a wide range of topics in the fields of both statistics and machine learning over the past three decades. His long-term research interests include identifying conditions for the convergence of deterministic and stochastic machine learning algorithms and investigating estimation and inference in the presence of possibly misspecified probability models.

Product Identifiers

Publisher
Taylor & Francis LTD
ISBN-13
9781138484696
eBay Product ID (ePID)
13046562993

Product Key Features

Author
Richard Golden
Publication Name
Statistical Machine Learning: a Unified Framework
Format
Hardcover
Language
English
Subject
Computer Science, Mathematics
Publication Year
2020
Type
Textbook
Number of Pages
506 Pages

Dimensions

Item Height
254mm
Item Width
178mm
Item Weight
1438g

Additional Product Features

Title_Author
Richard Golden
Series Title
Chapman & Hall/Crc Texts in Statistical Science
Country/Region of Manufacture
United Kingdom

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Handelsregistratienummer:
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KVK-nummer:
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