A computational approach to statistical learning (Record no. 212352)

000 -LEADER
fixed length control field aam a22 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. (IMPRINT)
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 Table of contents:


1. Introduction

Computational approach

Statistical learning

Example

Prerequisites

How to read this book

Supplementary materials

Formalisms and terminology

Exercises


2. Linear Models

Introduction

Ordinary least squares

The normal equations

Solving least squares with the singular value decomposition

Directly solving the linear system

(*) Solving linear models with orthogonal projection

(*) Sensitivity analysis

(*) Relationship between numerical and statistical error

Implementation and notes

Application: Cancer incidence rates

Exercises


3. Ridge Regression and Principal Component Analysis

Variance in OLS

Ridge regression

(*) A Bayesian perspective

Principal component analysis

Implementation and notes

Application: NYC taxicab data

Exercises


4. Linear Smoothers

Non-linearity

Basis expansion

Kernel regression

Local regression

Regression splines

(*) Smoothing splines

(*) B-splines

Implementation and notes

Application: US census tract data

Exercises


5. Generalized Linear Models

Classification with linear models

Exponential families

Iteratively reweighted GLMs

(*) Numerical issues

(*) Multi-class regression

Implementation and notes

Application: Chicago crime prediction

Exercises


6. Additive Models

Multivariate linear smoothers

Curse of dimensionality

Additive models

(*) Additive models as linear models

(*) Standard errors in additive models

Implementation and notes

Application: NYC flights data

Exercises


7. Penalized Regression Models

Variable selection

Penalized regression with the `- and `-norms

Orthogonal data matrix

Convex optimization and the elastic net

Coordinate descent

(*) Active set screening using the KKT conditions

(*) The generalized elastic net model

Implementation and notes

Application: Amazon product reviews

Exercises


8. Neural Networks

Dense neural network architecture

Stochastic gradient descent

Backward propagation of errors

Implementing backpropagation

Recognizing hand written digits

(*) Improving SGD and regularization

(*) Classification with neural networks

(*) Convolutional neural networks

Implementation and notes

Application: Image classification with EMNIST

Exercises


9. Dimensionality Reduction

Unsupervised learning

Kernel functions

Kernel principal component analysis

Spectral clustering

t-Distributed stochastic neighbor embedding (t-SNE)

Autoencoders

Implementation and notes

Application: Classifying and visualizing fashion MNIST

Exercises


10. Computation in Practice

Reference implementations

Sparse matrices

Sparse generalized linear models

Computation on row chunks

Feature hashing

Data quality issues

Implementation and notes

Application

Exercises


A Matrix Algebra

A Vector spaces

A Matrices

A Other useful matrix decompositions

B Floating Point Arithmetic and Numerical Computation

B Floating point arithmetic

B Numerical sources of error

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.

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.

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 as entry element Machine learning - Mathematics
9 (RLIN) 382085
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Mathematical statistics
9 (RLIN) 382086
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Estimation theory
9 (RLIN) 382087
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
Item type Books
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Permanent location Current location Shelving location Date acquired Source of acquisition Cost, normal purchase price Item location Full call number Barcode Date last seen Cost, replacement price Koha item type
          Non-fiction Vikram Sarabhai Library Vikram Sarabhai Library On Display 2019-07-12 7 4.00 Slot 104 (0 Floor, West Wing) 006.31015195 A7C6 199731 2019-07-12 5507.08 Books

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