A computational approach to statistical learning (Record no. 397518)
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000 -LEADER | |
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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 |
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 |
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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 |