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Online 1. W11STATS315A01 : Modern Applied Statistics: Learning. 2011 Winter [2011]
 Stanford University. Department of Statistics (Sponsor)
 Stanford (Calif.), 2011
 Description
 Book — 1 text file
 Summary

Overview of supervised learning. Linear regression and related methods. Model selection, least angle regression and the lasso, stepwise methods. Classification. Linear discriminant analysis, logistic regression, and support vector machines (SVMs). Basis expansions, splines and regularization. Kernel methods. Generalized additive models. Kernel smoothing. Gaussian mixtures and the EM algorithm. Model assessment and selection: crossvalidation and the bootstrap. Pathwise coordinate descent. Sparse graphical models. Prerequisites: STATS 305, 306A,B or consent of instructor.
Overview of supervised learning. Linear regression and related methods. Model selection, least angle regression and the lasso, stepwise methods. Classification. Linear discriminant analysis, logistic regression, and support vector machines (SVMs). Basis expansions, splines and regularization. Kernel methods. Generalized additive models. Kernel smoothing. Gaussian mixtures and the EM algorithm. Model assessment and selection: crossvalidation and the bootstrap. Pathwise coordinate descent. Sparse graphical models. Prerequisites: STATS 305, 306A,B or consent of instructor.
Overview of supervised learning. Linear regression and related methods. Model selection, least angle regression and the lasso, stepwise methods. Classification. Linear discriminant analysis, logistic regression, and support vector machines (SVMs). Basis expansions, splines and regularization. Kernel methods. Generalized additive models. Kernel smoothing. Gaussian mixtures and the EM algorithm. Model assessment and selection: crossvalidation and the bootstrap. Pathwise coordinate descent. Sparse graphical models. Prerequisites: STATS 305, 306A,B or consent of instructor.  Collection
 Stanford University Syllabi
Online 2. F10STATS30501 : Introduction to Statistical Modeling. 2010 Fall [2010]
 Stanford University. Department of Statistics (Sponsor)
 Stanford (Calif.), 2010
 Description
 Book — 1 text file
 Summary

Review of univariate regression. Multiple regression. Geometry, subspaces, orthogonality, projections, normal equations, rank deficiency, estimable functions and GaussMarkov theorem. Computation via QR decomposition, GrammSchmidt orthogonalization and the SVD. Interpreting coefficients, collinearity, graphical displays. Fits and the Hat matrix, leverage and influence, diagnostics, weighted least squares and resistance. Model selection, Cp/Aic and crossvalidation, stepwise, lasso. Basis expansions, splines. Multivariate normal distribution theory. ANOVA: Sources of measurements, fixed and random effects, randomization. Emphasis on problem sets involving substantive computations with data sets. Prerequisites: consent of instructor, 116, 200, applied statistics course, CS 106A, MATH 114.
Review of univariate regression. Multiple regression. Geometry, subspaces, orthogonality, projections, normal equations, rank deficiency, estimable functions and GaussMarkov theorem. Computation via QR decomposition, GrammSchmidt orthogonalization and the SVD. Interpreting coefficients, collinearity, graphical displays. Fits and the Hat matrix, leverage and influence, diagnostics, weighted least squares and resistance. Model selection, Cp/Aic and crossvalidation, stepwise, lasso. Basis expansions, splines. Multivariate normal distribution theory. ANOVA: Sources of measurements, fixed and random effects, randomization. Emphasis on problem sets involving substantive computations with data sets. Prerequisites: consent of instructor, 116, 200, applied statistics course, CS 106A, MATH 114.
Review of univariate regression. Multiple regression. Geometry, subspaces, orthogonality, projections, normal equations, rank deficiency, estimable functions and GaussMarkov theorem. Computation via QR decomposition, GrammSchmidt orthogonalization and the SVD. Interpreting coefficients, collinearity, graphical displays. Fits and the Hat matrix, leverage and influence, diagnostics, weighted least squares and resistance. Model selection, Cp/Aic and crossvalidation, stepwise, lasso. Basis expansions, splines. Multivariate normal distribution theory. ANOVA: Sources of measurements, fixed and random effects, randomization. Emphasis on problem sets involving substantive computations with data sets. Prerequisites: consent of instructor, 116, 200, applied statistics course, CS 106A, MATH 114.  Collection
 Stanford University Syllabi
 Hastie, Trevor.
 Boca Raton : CRC Press, Taylor & Francis Group, [2015]
 Description
 Book — xv, 351 pages : illustrations (some color) ; 25 cm.
 Summary

 Introduction
 The Lasso for Linear Models Introduction The Lasso Estimator CrossValidation and Inference Computation of the Lasso Solution Degrees of Freedom Uniqueness of the Lasso Solutions A Glimpse at the Theory The Nonnegative Garrote q Penalties and Bayes Estimates Some Perspective
 Generalized Linear Models Introduction Logistic Regression Multiclass Logistic Regression LogLinear Models and the Poisson GLM Cox Proportional Hazards Models Support Vector Machines Computational Details and glmnet
 Generalizations of the Lasso Penalty Introduction The Elastic Net The Group Lasso Sparse Additive Models and the Group Lasso The Fused Lasso Nonconvex Penalties
 Optimization Methods Introduction Convex Optimality Conditions Gradient Descent Coordinate Descent A Simulation Study Least Angle Regression Alternating Direction Method of Multipliers MinorizationMaximization Algorithms Biconvexity and Alternating Minimization Screening Rules
 Statistical Inference The Bayesian Lasso The Bootstrap PostSelection Inference for the Lasso Inference via a Debiased Lasso Other Proposals for PostSelection Inference
 Matrix Decompositions, Approximations, and Completion Introduction The Singular Value Decomposition Missing Data and Matrix Completion ReducedRank Regression A General Matrix Regression Framework Penalized Matrix Decomposition Additive Matrix Decomposition
 Sparse Multivariate Methods Introduction Sparse Principal Components Analysis Sparse Canonical Correlation Analysis Sparse Linear Discriminant Analysis Sparse Clustering
 Graphs and Model Selection Introduction Basics of Graphical Models Graph Selection via Penalized Likelihood Graph Selection via Conditional Inference Graphical Models with Hidden Variables
 Signal Approximation and Compressed Sensing Introduction Signals and Sparse Representations Random Projection and Approximation Equivalence between
 0 and
 1 Recovery
 Theoretical Results for the Lasso Introduction Bounds on Lasso 2error Bounds on Prediction Error Support Recovery in Linear Regression Beyond the Basic Lasso
 Bibliography
 Author Index
 Index
 Bibliographic Notes and Exercises appear at the end of each chapter.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9781498712163 20190415
 Online
Business Library
Business Library  Status 

On reserve at Business Library  
QA275 .H38 2015  Unknown 2hour loan 
MGTECON63401, MGTECON63401
 Course
 MGTECON63401  Machine Learning and Causal Inference
 Instructor(s)
 Athey, Susan Carleton
 Course
 MGTECON63401  Machine Learning and Causal Inference
 Instructor(s)
 Wager, Stefan De Treville
 Hastie, Trevor author.
 Boca Raton : CRC Press, Taylor & Francis Group, 2015.
 Description
 Book — xv, 351 pages : illustrations (some color) ; 25 cm.
 Summary

 Introduction
 The Lasso for Linear Models Introduction The Lasso Estimator CrossValidation and Inference Computation of the Lasso Solution Degrees of Freedom Uniqueness of the Lasso Solutions A Glimpse at the Theory The Nonnegative Garrote q Penalties and Bayes Estimates Some Perspective
 Generalized Linear Models Introduction Logistic Regression Multiclass Logistic Regression LogLinear Models and the Poisson GLM Cox Proportional Hazards Models Support Vector Machines Computational Details and glmnet
 Generalizations of the Lasso Penalty Introduction The Elastic Net The Group Lasso Sparse Additive Models and the Group Lasso The Fused Lasso Nonconvex Penalties
 Optimization Methods Introduction Convex Optimality Conditions Gradient Descent Coordinate Descent A Simulation Study Least Angle Regression Alternating Direction Method of Multipliers MinorizationMaximization Algorithms Biconvexity and Alternating Minimization Screening Rules
 Statistical Inference The Bayesian Lasso The Bootstrap PostSelection Inference for the Lasso Inference via a Debiased Lasso Other Proposals for PostSelection Inference
 Matrix Decompositions, Approximations, and Completion Introduction The Singular Value Decomposition Missing Data and Matrix Completion ReducedRank Regression A General Matrix Regression Framework Penalized Matrix Decomposition Additive Matrix Decomposition
 Sparse Multivariate Methods Introduction Sparse Principal Components Analysis Sparse Canonical Correlation Analysis Sparse Linear Discriminant Analysis Sparse Clustering
 Graphs and Model Selection Introduction Basics of Graphical Models Graph Selection via Penalized Likelihood Graph Selection via Conditional Inference Graphical Models with Hidden Variables
 Signal Approximation and Compressed Sensing Introduction Signals and Sparse Representations Random Projection and Approximation Equivalence between
 0 and
 1 Recovery
 Theoretical Results for the Lasso Introduction Bounds on Lasso 2error Bounds on Prediction Error Support Recovery in Linear Regression Beyond the Basic Lasso
 Bibliography
 Author Index
 Index
 Bibliographic Notes and Exercises appear at the end of each chapter.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9781498712163 20190415
Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

Stacks  
QA275 .H38 2015  Unknown 
 Hastie, Trevor.
 2nd ed.  New York : Springer, c2009.
 Description
 Book — xxii, 745 p. : ill. ; 24 cm.
 Summary

 Introduction. Overview of supervised learning. Linear methods for regression. Linear methods for classification. Basis expansions and regularization. Kernel smoothing methods. Model assessment and selection. Model inference and averaging. Additive models, trees, and related methods. Boosting and additive trees. Neural networks. Support vector machines and flexible discriminants. Prototype methods and nearestneighbors. Unsupervised learning.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9780387848570 20190415
 Online

 dx.doi.org SpringerLink
 Google Books (Full view)
Marine Biology Library (Miller), Science Library (Li and Ma)
Marine Biology Library (Miller)  Status 

Missing  
Q325.75 .H37 2009  Unavailable 
Stacks  
Q325.75 .H37 2009  Unknown 
Science Library (Li and Ma)  Status 

Stacks  
Q325.75 .H37 2009  Unknown On reserve at Li and Ma Science Library 2hour loan 
Q325.75 .H37 2009  Unknown On reserve at Li and Ma Science Library 2hour loan 
Q325.75 .H37 2009  Unknown On reserve at Li and Ma Science Library 2hour loan 
STATS305C01, STATS315B01
 Course
 STATS305C01  Applied Statistics III
 Instructor(s)
 Taylor, Jonathan E.
 Course
 STATS315B01  Modern Applied Statistics: Data Mining
 Instructor(s)
 Friedman, Jerry H
 Hastie, Trevor author.
 2nd ed.  New York : Springer, [2009]
 Description
 Book — xxii, 745 pages : illustrations (some color) ; 24 cm.
 Summary

 Introduction
 Overview of supervised learning
 Linear methods for regression
 Linear methods for classification
 Basis expansions and regularization
 Kernel smoothing methods
 Model assessment and selection
 Model inference and averaging
 Additive models, trees, and related methods
 Boosting and additive trees
 Neural networks
 Support vector machines and flexible discriminants
 Prototype methods and nearestneighbors
 Unsupervised learning
 Random forests
 Ensemble learning
 Undirected graphical models
 Highdimensional problems : p>> N.
(source: Nielsen Book Data) 9780387848570 20190415
 Online
 Hastie, Trevor, author.
 Second edition.  New York : Springer, [2009]
 Description
 Book — xxii, 745 pages : illustrations (some color), charts ; 24 cm.
 Summary

 1. Introduction
 2. Overview of supervised learning
 3. Linear methods for regression
 4. Linear methods for classification
 5. Basis expansions and regularization
 6. Kernel smoothing methods
 7. Model assessment and selection
 8. Model inference and averaging
 9. Additive models, trees, and related methods
 10. Boosting and additive trees
 11. Neural networks
 12. Support vector machines and flexible discriminants
 13. Prototype methods and nearestneighbors
 14. Unsupervised learning
 15. Random forests
 16. Ensemble learning
 17. Undirected graphical models
 18. Highdimensional problems: p>> N.
(source: Nielsen Book Data) 9780387848570 20190415
Business Library
Business Library  Status 

On reserve at Business Library  
Q325.75 .H37 2009  Unknown 2hour loan 
MGTECON63401, MGTECON63401
 Course
 MGTECON63401  Machine Learning and Causal Inference
 Instructor(s)
 Athey, Susan Carleton
 Course
 MGTECON63401  Machine Learning and Causal Inference
 Instructor(s)
 Wager, Stefan De Treville
 Hastie, Trevor.
 New York : Springer, c2001.
 Description
 Book — xvi, 533 p. : ill. (some col.) ; 25 cm.
 Summary

 Overview of Supervised Learning. Linear Methods for Regression. Linear Methods for Classification. Basic Expansions and Regularization. Kernel Methods. Model Assessment and Selection. Model Inference and Averaging. Additive Models, Trees, and Related Methods. Boosting and Additive Trees. Neural Networks. Support Vector Machines and Flexible Discriminates. Prototype Methods and Nearest Neighbors. Unsupervised Learning.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9780387952840 20160527
Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

Stacks  
Q325.75 .H37 2001  Unknown 
Q325.75 .H37 2001  Unknown 
 Hastie, Trevor.
 New York : Springer, c2001.
 Description
 Book — xvi, 533 p. : col. ill. ; 25 cm.
 Summary

 Overview of Supervised Learning. Linear Methods for Regression. Linear Methods for Classification. Basic Expansions and Regularization. Kernel Methods. Model Assessment and Selection. Model Inference and Averaging. Additive Models, Trees, and Related Methods. Boosting and Additive Trees. Neural Networks. Support Vector Machines and Flexible Discriminates. Prototype Methods and Nearest Neighbors. Unsupervised Learning.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9780387952840 20160527
 Online
 Hastie, Trevor
 New York : Springer, c2001.
 Description
 Book — xvi, 533 pages : illustrations (some color) ; 25 cm
 Online
Medical Library (Lane)
Medical Library (Lane)  Status 

Check Lane Library catalog for status  
Q325.75 .F75 2001  Unknown 
Online 11. Bayesian backfitting [1998]
 Hastie, Trevor.
 Stanford, Calif. : Stanford University, Division of Biostatistics, 1998.
 Description
 Book — 1 online resource (38 pages)
 Also online at

12. Bayesian backfitting [1998]
 Hastie, Trevor.
 Stanford, Calif. : Stanford University, Division of Biostatistics, 1998.
 Description
 Book — 38 p. : ill ; 28 cm.
 Online
SAL3 (offcampus storage), Special Collections
SAL3 (offcampus storage)  Status 

Stacks  Request 
262197  Available 
262197  Available 
Special Collections  Status 

University Archives  Request onsite access 
262197  Inlibrary use 
Online 13. Classification by pairwise coupling [1997]
Online 14. Flexible discriminant and mixture models [1997]
Online 15. Discriminant adaptive nearest neighbor classification [1994]
Online 16. Discriminant adaptive nearest neighbor classification [1994]
17. Generalized additive models [1990]
 Hastie, Trevor.
 1st ed.  London ; New York : Chapman and Hall, 1990.
 Description
 Book — xv, 335 p. : ill. ; 23 cm.
 Summary

 Preface. Introduction. Smoothing. Smoothing in Detail. Additive Models. Some Theory for Additive Models. Generalized Additive Models. Response Transformation Models. Extensions to Other Settings. Further Topics. Case Studies. Appendices. References. Author Index. Subject Index.
 (source: Nielsen Book Data)
(source: Nielsen Book Data) 9780412343902 20160528
 Online
Online 18. Generalized additive models, cubic splines and penalized likelihood [1987]
 Hastie, Trevor.
 May 22, 1987.
 Description
 Book — 1 online resource (20 pages) Digital: text file.
 Also online at

Online 19. Generalized additive models [1984]
Online 20. Generalized additive models [1984]
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