Statistical Machine Learning: A Unified Framework

Statistical Machine Learning: A Unified Framework (Chapman & Hall/CRC Texts in Statistical Science)
By 作者:Richard Golden
Hardcover : 524 pages
ISBN-10 : 1138484695
ISBN-13 : 9781138484696
Product Dimensions : 17.53 x 2.54 x 25.65 cm
Publisher Finelybook 出版社 : Chapman and Hall/CRC (14 July 2020)
Language 语言: : English
The Book Description robot was collected from Amazon and arranged by Finelybook
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.

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.


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