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The most crucial ability for machine learning and data science is
mathematical logic for grasping their essence rather than knowledge
and experience. This textbook approaches the essence of machine
learning and data science by considering math problems and building
Python programs. As the preliminary part, Chapter 1 provides a
concise introduction to linear algebra, which will help novices
read further to the following main chapters. Those succeeding
chapters present essential topics in statistical learning: linear
regression, classification, resampling, information criteria,
regularization, nonlinear regression, decision trees, support
vector machines, and unsupervised learning. Each chapter
mathematically formulates and solves machine learning problems and
builds the programs. The body of a chapter is accompanied by proofs
and programs in an appendix, with exercises at the end of the
chapter. Because the book is carefully organized to provide the
solutions to the exercises in each chapter, readers can solve the
total of 100 exercises by simply following the contents of each
chapter. This textbook is suitable for an undergraduate or graduate
course consisting of about 12 lectures. Written in an
easy-to-follow and self-contained style, this book will also be
perfect material for independent learning.
The most crucial ability for machine learning and data science is
mathematical logic for grasping their essence rather than knowledge
and experience. This textbook approaches the essence of sparse
estimation by considering math problems and building R programs.
Each chapter introduces the notion of sparsity and provides
procedures followed by mathematical derivations and source programs
with examples of execution. To maximize readers' insights into
sparsity, mathematical proofs are presented for almost all
propositions, and programs are described without depending on any
packages. The book is carefully organized to provide the solutions
to the exercises in each chapter so that readers can solve the
total of 100 exercises by simply following the contents of each
chapter. This textbook is suitable for an undergraduate or graduate
course consisting of about 15 lectures (90 mins each). Written in
an easy-to-follow and self-contained style, this book will also be
perfect material for independent learning by data scientists,
machine learning engineers, and researchers interested in linear
regression, generalized linear lasso, group lasso, fused lasso,
graphical models, matrix decomposition, and multivariate analysis.
This book is one of a series of textbooks in machine learning by
the same author. Other titles are: - Statistical Learning with Math
and R (https://www.springer.com/gp/book/9789811575679) -
Statistical Learning with Math and Python
(https://www.springer.com/gp/book/9789811578762) - Sparse
Estimation with Math and Python
Master the art of machine learning and data science by diving into
the essence of mathematical logic with this comprehensive
textbook. This book focuses on the widely applicable
information criterion (WAIC), also described as the Watanabe-Akaike
information criterion, and the widely applicable Bayesian
information criterion (WBIC), also described as the Watanabe
Bayesian information criterion. The book expertly guides you
through relevant mathematical problems while also providing
hands-on experience with programming in Python and Stan. Whether
you’re a data scientist looking to refine your model selection
process or a researcher who wants to explore the latest
developments in Bayesian statistics, this accessible guide will
give you a firm grasp of Watanabe Bayesian Theory.The key features
of this indispensable book include: A clear and self-contained
writing style, ensuring ease of understanding for readers at
various levels of expertise. 100 carefully selected exercises
accompanied by solutions in the main text, enabling readers to
effectively gauge their progress and comprehension. A comprehensive
guide to Sumio Watanabe’s groundbreaking Bayes theory,
demystifying a subject once considered too challenging even for
seasoned statisticians. Detailed source programs and Stan codes
that will enhance readers’ grasp of the mathematical concepts
presented. A streamlined approach to algebraic geometry topics in
Chapter 6, making Bayes theory more accessible and less daunting.
Embark on your machine learning and data science journey with this
essential textbook and unlock the full potential of WAIC and WBIC
today!
Master the art of machine learning and data science by diving into
the essence of mathematical logic with this comprehensive textbook.
This book focuses on the widely applicable information criterion
(WAIC), also described as the Watanabe-Akaike information
criterion, and the widely applicable Bayesian information criterion
(WBIC), also described as the Watanabe Bayesian information
criterion. This book expertly guides you through relevant
mathematical problems while also providing hands-on experience with
programming in R and Stan. Whether you’re a data scientist
looking to refine your model selection process or a researcher who
wants to explore the latest developments in Bayesian statistics,
this accessible guide will give you a firm grasp of Watanabe
Bayesian Theory.The key features of this indispensable book
include: A clear and self-contained writing style, ensuring ease of
understanding for readers at various levels of expertise. 100
carefully selected exercises accompanied by solutions in the main
text, enabling readers to effectively gauge their progress and
comprehension. A comprehensive guide to Sumio Watanabe’s
groundbreaking Bayes theory, demystifying a subject once considered
too challenging even for seasoned statisticians. Detailed source
programs and Stan codes that will enhance readers’ grasp of the
mathematical concepts presented. A streamlined approach to
algebraic geometry topics in Chapter 6, making Bayes theory more
accessible and less daunting. Embark on your machine learning and
data science journey with this essential textbook and unlock the
full potential of WAIC and WBIC today!
The most crucial ability for machine learning and data science is
mathematical logic for grasping their essence rather than relying
on knowledge or experience. This textbook addresses the
fundamentals of kernel methods for machine learning by considering
relevant math problems and building R programs. The book's main
features are as follows: The content is written in an
easy-to-follow and self-contained style. The book includes 100
exercises, which have been carefully selected and refined. As their
solutions are provided in the main text, readers can solve all of
the exercises by reading the book. The mathematical premises of
kernels are proven and the correct conclusions are provided,
helping readers to understand the nature of kernels. Source
programs and running examples are presented to help readers acquire
a deeper understanding of the mathematics used. Once readers have a
basic understanding of the functional analysis topics covered in
Chapter 2, the applications are discussed in the subsequent
chapters. Here, no prior knowledge of mathematics is assumed. This
book considers both the kernel for reproducing kernel Hilbert space
(RKHS) and the kernel for the Gaussian process; a clear distinction
is made between the two.
The most crucial ability for machine learning and data science is
mathematical logic for grasping their essence rather than relying
on knowledge or experience. This textbook addresses the
fundamentals of kernel methods for machine learning by considering
relevant math problems and building Python programs. The book's
main features are as follows: The content is written in an
easy-to-follow and self-contained style. The book includes 100
exercises, which have been carefully selected and refined. As their
solutions are provided in the main text, readers can solve all of
the exercises by reading the book. The mathematical premises of
kernels are proven and the correct conclusions are provided,
helping readers to understand the nature of kernels. Source
programs and running examples are presented to help readers acquire
a deeper understanding of the mathematics used. Once readers have a
basic understanding of the functional analysis topics covered in
Chapter 2, the applications are discussed in the subsequent
chapters. Here, no prior knowledge of mathematics is assumed. This
book considers both the kernel for reproducing kernel Hilbert space
(RKHS) and the kernel for the Gaussian process; a clear distinction
is made between the two.
The most crucial ability for machine learning and data science is
mathematical logic for grasping their essence rather than knowledge
and experience. This textbook approaches the essence of machine
learning and data science by considering math problems and building
R programs. As the preliminary part, Chapter 1 provides a concise
introduction to linear algebra, which will help novices read
further to the following main chapters. Those succeeding chapters
present essential topics in statistical learning: linear
regression, classification, resampling, information criteria,
regularization, nonlinear regression, decision trees, support
vector machines, and unsupervised learning. Each chapter
mathematically formulates and solves machine learning problems and
builds the programs. The body of a chapter is accompanied by proofs
and programs in an appendix, with exercises at the end of the
chapter. Because the book is carefully organized to provide the
solutions to the exercises in each chapter, readers can solve the
total of 100 exercises by simply following the contents of each
chapter. This textbook is suitable for an undergraduate or graduate
course consisting of about 12 lectures. Written in an
easy-to-follow and self-contained style, this book will also be
perfect material for independent learning.
The most crucial ability for machine learning and data science is
mathematical logic for grasping their essence rather than knowledge
and experience. This textbook approaches the essence of sparse
estimation by considering math problems and building Python
programs. Each chapter introduces the notion of sparsity and
provides procedures followed by mathematical derivations and source
programs with examples of execution. To maximize readers' insights
into sparsity, mathematical proofs are presented for almost all
propositions, and programs are described without depending on any
packages. The book is carefully organized to provide the solutions
to the exercises in each chapter so that readers can solve the
total of 100 exercises by simply following the contents of each
chapter. This textbook is suitable for an undergraduate or graduate
course consisting of about 15 lectures (90 mins each). Written in
an easy-to-follow and self-contained style, this book will also be
perfect material for independent learning by data scientists,
machine learning engineers, and researchers interested in linear
regression, generalized linear lasso, group lasso, fused lasso,
graphical models, matrix decomposition, and multivariate analysis.
This book is one of a series of textbooks in machine learning by
the same Author. Other titles are: Statistical Learning with Math
and R (https://www.springer.com/gp/book/9789811575679) Statistical
Learning with Math and Pyth
(https://www.springer.com/gp/book/9789811578762) Sparse Estimation
with Math and R
This volume constitutes the refereed proceedings of the Second
International Workshop on Advanced Methodologies for Bayesian
Networks, AMBN 2015, held in Yokohama, Japan, in November 2015. The
18 revised full papers and 6 invited abstracts presented were
carefully reviewed and selected from numerous submissions. In the
International Workshop on Advanced Methodologies for Bayesian
Networks (AMBN), the researchers explore methodologies for
enhancing the effectiveness of graphical models including modeling,
reasoning, model selection, logic-probability relations, and
causality. The exploration of methodologies is complemented
discussions of practical considerations for applying graphical
models in real world settings, covering concerns like scalability,
incremental learning, parallelization, and so on.
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