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Sparse Estimation with Math and R

2021-08-04 Computers
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Release : 2021-08-04
Genre : Computers
Kind : eBook
Book Rating : 466/5 ( reviews)

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Book Synopsis Sparse Estimation with Math and R by : Joe Suzuki

Download or read book Sparse Estimation with Math and R written by Joe Suzuki. This book was released on 2021-08-04. Available in PDF, EPUB and Kindle. Book excerpt: 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

Sparse Estimation with Math and Python

2021-10-30 Computers
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Release : 2021-10-30
Genre : Computers
Kind : eBook
Book Rating : 385/5 ( reviews)

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Book Synopsis Sparse Estimation with Math and Python by : Joe Suzuki

Download or read book Sparse Estimation with Math and Python written by Joe Suzuki. This book was released on 2021-10-30. Available in PDF, EPUB and Kindle. Book excerpt: 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

Fundamentals of High-Dimensional Statistics

2021-11-16 Mathematics
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Release : 2021-11-16
Genre : Mathematics
Kind : eBook
Book Rating : 926/5 ( reviews)

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Book Synopsis Fundamentals of High-Dimensional Statistics by : Johannes Lederer

Download or read book Fundamentals of High-Dimensional Statistics written by Johannes Lederer. This book was released on 2021-11-16. Available in PDF, EPUB and Kindle. Book excerpt: This textbook provides a step-by-step introduction to the tools and principles of high-dimensional statistics. Each chapter is complemented by numerous exercises, many of them with detailed solutions, and computer labs in R that convey valuable practical insights. The book covers the theory and practice of high-dimensional linear regression, graphical models, and inference, ensuring readers have a smooth start in the field. It also offers suggestions for further reading. Given its scope, the textbook is intended for beginning graduate and advanced undergraduate students in statistics, biostatistics, and bioinformatics, though it will be equally useful to a broader audience.

Computational Network Analysis with R

2016-07-22 Medical
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Release : 2016-07-22
Genre : Medical
Kind : eBook
Book Rating : 404/5 ( reviews)

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Book Synopsis Computational Network Analysis with R by : Matthias Dehmer

Download or read book Computational Network Analysis with R written by Matthias Dehmer. This book was released on 2016-07-22. Available in PDF, EPUB and Kindle. Book excerpt: This new title in the well-established "Quantitative Network Biology" series includes innovative and existing methods for analyzing network data in such areas as network biology and chemoinformatics. With its easy-to-follow introduction to the theoretical background and application-oriented chapters, the book demonstrates that R is a powerful language for statistically analyzing networks and for solving such large-scale phenomena as network sampling and bootstrapping. Written by editors and authors with an excellent track record in the field, this is the ultimate reference for R in Network Analysis.

Oracle Inequalities in Empirical Risk Minimization and Sparse Recovery Problems

2011-07-29 Mathematics
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Release : 2011-07-29
Genre : Mathematics
Kind : eBook
Book Rating : 475/5 ( reviews)

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Book Synopsis Oracle Inequalities in Empirical Risk Minimization and Sparse Recovery Problems by : Vladimir Koltchinskii

Download or read book Oracle Inequalities in Empirical Risk Minimization and Sparse Recovery Problems written by Vladimir Koltchinskii. This book was released on 2011-07-29. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of these lecture notes is to provide an introduction to the general theory of empirical risk minimization with an emphasis on excess risk bounds and oracle inequalities in penalized problems. In recent years, there have been new developments in this area motivated by the study of new classes of methods in machine learning such as large margin classification methods (boosting, kernel machines). The main probabilistic tools involved in the analysis of these problems are concentration and deviation inequalities by Talagrand along with other methods of empirical processes theory (symmetrization inequalities, contraction inequality for Rademacher sums, entropy and generic chaining bounds). Sparse recovery based on l_1-type penalization and low rank matrix recovery based on the nuclear norm penalization are other active areas of research, where the main problems can be stated in the framework of penalized empirical risk minimization, and concentration inequalities and empirical processes tools have proved to be very useful.

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