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The Elements of Statistical Learning Data Mining Inference and Prediction 2nd Edition by Trevor Hastie, Robert Tibshirani, Jerome Friedman ISBN 0387848576 9780387848570

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The Elements of Statistical Learning Data Mining Inference and Prediction Second Edition Trevor Hastie Digital Instant Download

Author(s): Trevor Hastie , Robert Tibshirani , Jerome Friedman
ISBN(s): 9780387848570, 0387848576
Edition: 2nd
File Details: PDF, 20.64 MB
Year: 2009
Language: english
SKU: EB-23630602 Category: Tags: , ,

The Elements of Statistical Learning Data Mining Inference and Prediction 2nd Edition by Trevor Hastie, Robert Tibshirani, Jerome Friedman – Ebook PDF Instant Download/Delivery: 0387848576, 9780387848570
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ISBN 10: 0387848576
ISBN 13: 9780387848570
Author: Trevor Hastie, Robert Tibshirani, Jerome Friedman

During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book’s coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting—the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for “wide” data (p bigger than n), including multiple testing and false discovery rates.

The Elements of Statistical Learning Data Mining Inference and Prediction 2nd Table of contents:

1. Introduction
1.1 The Curse of Dimensionality
1.2 The Bias-Variance Trade-off
1.3 Statistical Learning: An Overview

2. Overview of Supervised Learning
2.1 Supervised Learning: A General Framework
2.2 Linear Methods for Regression
2.3 Linear Methods for Classification
2.4 Basis Expansion and Regularization
2.5 Support Vector Machines
2.6 Additive Models, Trees, and Related Methods

3. Linear Methods for Regression
3.1 The Linear Model
3.2 Least Squares and Maximum Likelihood
3.3 Ridge Regression
3.4 The Lasso
3.5 Elastic Net
3.6 The Effect of Regularization on Predictions

4. Linear Methods for Classification
4.1 Linear Discriminants
4.2 Logistic Regression
4.3 Regularization in Logistic Regression
4.4 The Support Vector Machine: A Geometric View
4.5 The Support Vector Machine: A Probabilistic View

5. Basis Expansions and Regularization
5.1 Polynomial Regression
5.2 Regularization: The Bias-Variance Trade-off
5.3 Smoothing Splines
5.4 Generalized Additive Models
5.5 The Lasso and Other Regularization Methods

6. Kernel Methods
6.1 The Kernel Trick
6.2 Support Vector Machines: A Kernel Perspective
6.3 The Connection Between SVM and Kernel Ridge Regression
6.4 Reproducing Kernel Hilbert Spaces
6.5 Kernel PCA
6.6 Nonlinear Dimension Reduction

7. Model Assessment and Selection
7.1 Cross-Validation
7.2 The Bias-Variance Decomposition
7.3 Model Selection and Selection Criteria
7.4 The Bootstrap
7.5 AIC, BIC, and Related Criteria

8. Model Inference and Averaging
8.1 The Bias-Variance Decomposition and Model Complexity
8.2 Inference: Confidence Intervals and Hypothesis Tests
8.3 Bayesian Inference
8.4 The Bayesian Framework for Inference
8.5 Model Averaging
8.6 Bootstrap and Model Averaging

9. Additive Models, Trees, and Related Methods
9.1 The Basic Additive Model
9.2 Regression Trees
9.3 Classification Trees
9.4 Boosting and the AdaBoost Algorithm
9.5 Gradient Boosting Machines
9.6 Random Forests

10. Neural Networks
10.1 Introduction to Neural Networks
10.2 The Perceptron
10.3 Feed-Forward Networks
10.4 The Backpropagation Algorithm
10.5 Overfitting and Regularization in Neural Networks
10.6 Convolutional Neural Networks (CNNs)

11. Support Vector Machines and Flexible Discriminants
11.1 Support Vector Machines: The Geometric View
11.2 The Support Vector Machine: A Probabilistic View
11.3 The Support Vector Machine: Duality and the Kernel Trick
11.4 Flexible Discriminants: Nonlinear SVM
11.5 The Margin and the Hard-Margin SVM

12. Unsupervised Learning
12.1 Clustering: K-Means and K-Medoids
12.2 Hierarchical Clustering
12.3 Gaussian Mixture Models
12.4 Principal Component Analysis (PCA)
12.5 Multidimensional Scaling
12.6 Independent Component Analysis

13. Random Forests and Ensemble Methods
13.1 Introduction to Ensemble Learning
13.2 Random Forests
13.3 Boosting and AdaBoost
13.4 Stochastic Gradient Boosting
13.5 Random Forests for Classification and Regression
13.6 Bias-Variance Trade-off in Ensemble Methods

14. High-Dimensional Problems: The Large p, Small n Case
14.1 The Curse of Dimensionality
14.2 Feature Selection
14.3 Regularization in High Dimensions
14.4 The Lasso in High Dimensions
14.5 Sparse Modeling: Compressed Sensing
14.6 High-Dimensional Inference

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Tags: Trevor Hastie, Robert Tibshirani, Jerome Friedman, Statistical