A computational approach to statistical learning (Record no. 397518)

MARC details
000 -LEADER
fixed length control field 05122aam a2200229 4500
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 190712b ||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781138046375
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.31015195
Item number A7C6
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Arnold, Taylor
9 (RLIN) 382084
245 ## - TITLE STATEMENT
Title A computational approach to statistical learning
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Name of publisher, distributor, etc. CRC Press
Date of publication, distribution, etc. 2019
Place of publication, distribution, etc. Boca Raton
300 ## - PHYSICAL DESCRIPTION
Extent xiii, 361 p.
Other physical details Includes bibliographical references and index
440 ## - SERIES STATEMENT/ADDED ENTRY--TITLE
Title Chapman & hall/ CRC texts in statistical science
9 (RLIN) 372853
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc. note Table of contents:<br/><br/><br/>1. Introduction<br/><br/>Computational approach<br/><br/>Statistical learning<br/><br/>Example<br/><br/>Prerequisites<br/><br/>How to read this book<br/><br/>Supplementary materials<br/><br/>Formalisms and terminology<br/><br/>Exercises<br/><br/><br/>2. Linear Models<br/><br/>Introduction<br/><br/>Ordinary least squares<br/><br/>The normal equations<br/><br/>Solving least squares with the singular value decomposition<br/><br/>Directly solving the linear system<br/><br/>(*) Solving linear models with orthogonal projection<br/><br/>(*) Sensitivity analysis<br/><br/>(*) Relationship between numerical and statistical error<br/><br/>Implementation and notes<br/><br/>Application: Cancer incidence rates<br/><br/>Exercises<br/><br/><br/>3. Ridge Regression and Principal Component Analysis<br/><br/>Variance in OLS<br/><br/>Ridge regression<br/><br/>(*) A Bayesian perspective<br/><br/>Principal component analysis<br/><br/>Implementation and notes<br/><br/>Application: NYC taxicab data<br/><br/>Exercises<br/><br/><br/>4. Linear Smoothers<br/><br/>Non-linearity<br/><br/>Basis expansion<br/><br/>Kernel regression<br/><br/>Local regression<br/><br/>Regression splines<br/><br/>(*) Smoothing splines<br/><br/>(*) B-splines<br/><br/>Implementation and notes<br/><br/>Application: US census tract data<br/><br/>Exercises<br/><br/><br/>5. Generalized Linear Models<br/><br/>Classification with linear models<br/><br/>Exponential families<br/><br/>Iteratively reweighted GLMs<br/><br/>(*) Numerical issues<br/><br/>(*) Multi-class regression<br/><br/>Implementation and notes<br/><br/>Application: Chicago crime prediction<br/><br/>Exercises<br/><br/><br/>6. Additive Models<br/><br/>Multivariate linear smoothers<br/><br/>Curse of dimensionality<br/><br/>Additive models<br/><br/>(*) Additive models as linear models<br/><br/>(*) Standard errors in additive models<br/><br/>Implementation and notes<br/><br/>Application: NYC flights data<br/><br/>Exercises<br/><br/><br/>7. Penalized Regression Models<br/><br/>Variable selection<br/><br/>Penalized regression with the `- and `-norms<br/><br/>Orthogonal data matrix<br/><br/>Convex optimization and the elastic net<br/><br/>Coordinate descent<br/><br/>(*) Active set screening using the KKT conditions<br/><br/>(*) The generalized elastic net model<br/><br/>Implementation and notes<br/><br/>Application: Amazon product reviews<br/><br/>Exercises<br/><br/><br/>8. Neural Networks<br/><br/>Dense neural network architecture<br/><br/>Stochastic gradient descent<br/><br/>Backward propagation of errors<br/><br/>Implementing backpropagation<br/><br/>Recognizing hand written digits<br/><br/>(*) Improving SGD and regularization<br/><br/>(*) Classification with neural networks<br/><br/>(*) Convolutional neural networks<br/><br/>Implementation and notes<br/><br/>Application: Image classification with EMNIST<br/><br/>Exercises<br/><br/><br/>9. Dimensionality Reduction<br/><br/>Unsupervised learning<br/><br/>Kernel functions<br/><br/>Kernel principal component analysis<br/><br/>Spectral clustering<br/><br/>t-Distributed stochastic neighbor embedding (t-SNE)<br/><br/>Autoencoders<br/><br/>Implementation and notes<br/><br/>Application: Classifying and visualizing fashion MNIST<br/><br/>Exercises<br/><br/><br/>10. Computation in Practice<br/><br/>Reference implementations<br/><br/>Sparse matrices<br/><br/>Sparse generalized linear models<br/><br/>Computation on row chunks<br/><br/>Feature hashing<br/><br/>Data quality issues<br/><br/>Implementation and notes<br/><br/>Application<br/><br/>Exercises<br/><br/><br/>A Matrix Algebra<br/><br/>A Vector spaces<br/><br/>A Matrices<br/><br/>A Other useful matrix decompositions<br/><br/>B Floating Point Arithmetic and Numerical Computation<br/><br/>B Floating point arithmetic<br/><br/>B Numerical sources of error<br/><br/>B Computational effort
520 ## - SUMMARY, ETC.
Summary, etc. A Computational Approach to Statistical Learning gives a novel introduction to predictive modeling by focusing on the algorithmic and numeric motivations behind popular statistical methods. The text contains annotated code to over 80 original reference functions. These functions provide minimal working implementations of common statistical learning algorithms. Every chapter concludes with a fully worked out application that illustrates predictive modeling tasks using a real-world dataset.<br/><br/>The text begins with a detailed analysis of linear models and ordinary least squares. Subsequent chapters explore extensions such as ridge regression, generalized linear models, and additive models. The second half focuses on the use of general-purpose algorithms for convex optimization and their application to tasks in statistical learning. Models covered include the elastic net, dense neural networks, convolutional neural networks (CNNs), and spectral clustering. A unifying theme throughout the text is the use of optimization theory in the description of predictive models, with a particular focus on the singular value decomposition (SVD). Through this theme, the computational approach motivates and clarifies the relationships between various predictive models.<br/><br/>https://www.crcpress.com/A-Computational-Approach-to-Statistical-Learning/Arnold-Kane-Lewis/p/book/9781138046375
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Machine learning - Mathematics
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Mathematical statistics
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Estimation theory
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Michael, Kane
Relator term Co author
9 (RLIN) 382088
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Lewis, Bryan W.
Relator term Co author
9 (RLIN) 382089
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type Book
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Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Home library Current library Shelving location Date acquired Source of acquisition Cost, normal purchase price Total Checkouts Full call number Barcode Date last seen Cost, replacement price Price effective from Koha item type
    Dewey Decimal Classification     Non-fiction Ahmedabad Ahmedabad General Stacks 12/07/2019 7 4.00   006.31015195 A7C6 199731 12/07/2019 5507.08 12/07/2019 Book

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