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Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
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Statistical Machine Learning - A Unified Framework (Hardcover)
Loot Price: R3,409
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Statistical Machine Learning - A Unified Framework (Hardcover)
Series: Chapman & Hall/CRC Texts in Statistical Science
Expected to ship within 10 - 15 working days
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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.
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