Elements of Data Science, Machine Learning, and Artificial Intelligence Using R / Frank Emmert-Streib, Salissou Moutari, and Matthias Dehmer.

Format:

Book

Author/Creator:

Emmert-Streib, Frank, author.

Moutari, Salissou, author.

Dehmer, Matthias, author.

Series:

Intelligent Technologies and Robotics Series

Language:

English

Subjects (All):

Artificial intelligence.

Machine learning.

R (Computer program language).

Physical Description:

1 online resource (582 pages)

Edition:

First edition.

Place of Publication:

Cham, Switzerland : Springer, [2023]

Summary:

The textbook provides students with tools they need to analyze complex data using methods from data science, machine learning and artificial intelligence.The authors include both the presentation of methods along with applications using the programming language R, which is the gold standard for analyzing data.

Contents:

Intro

Preface

Contents

1 Introduction to Learning from Data

1.1 What Is Data Science?

1.2 Converting Data into Knowledge

1.2.1 Big Aims: Big Questions

1.2.2 Generating Insights by Visualization

1.3 Structure of the Book

1.3.1 Part I

1.3.2 Part II

1.3.3 Part III

1.4 Our Motivation for Writing This Book

1.5 How to Use This Book

1.6 Summary

Part I General Topics

2 General Prediction Models

2.1 Introduction

2.2 Categorization of Methods

2.2.1 Properties of the Data

2.2.2 Properties of the Optimization Algorithm

2.2.3 Properties of the Model

2.2.4 Summary

2.3 Overview of Prediction Models

2.4 Causal Model versus Predictive Model

2.5 Explainable AI

2.6 Fundamental Statistical Characteristics of Prediction Models

2.6.1 Example

2.7 Summary

2.8 Exercises

3 General Error Measures

3.1 Introduction

3.2 Motivation

3.3 Fundamental Error Measures

3.4 Error Measures

3.4.1 True-Positive Rate and True-Negative Rate

3.4.2 Positive Predictive Value and Negative Predictive Value

3.4.3 Accuracy

3.4.4 F-Score

3.4.5 False Discovery Rate and False Omission Rate

3.4.6 False-Negative Rate and False-Positive Rate

3.4.7 Matthews Correlation Coefficient

3.4.8 Cohen's Kappa

3.4.9 Normalized Mutual Information

3.4.10 Area Under the Receiver Operator Characteristic Curve

3.5 Evaluation of Outcome

3.5.1 Evaluation of an Individual Method

3.5.2 Comparing Multiple Binary Decision-Making Methods

3.6 Summary

3.7 Exercises

4 Resampling Methods

4.1 Introduction

4.2 Resampling Methods for Error Estimation

4.2.1 Holdout Set

4.2.2 Leave-One-Out CV

4.2.3 K-Fold Cross-Validation

4.3 Extended Resampling Methods for Error Estimation

4.3.1 Repeated Holdout Set

4.3.2 Repeated K-Fold CV

4.3.3 Stratified K-Fold CV.

4.4 Bootstrap

4.4.1 Resampling With versus Resampling Without Replacement

4.5 Subsampling

4.6 Different Types of Prediction Data Sets

4.7 Sampling from a Distribution

4.8 Standard Error

4.9 Summary

4.10 Exercises

5 Data

5.1 Introduction

5.2 Data Types

5.2.1 Genomic Data

5.2.2 Network Data

5.2.3 Text Data

5.2.4 Time-to-Event Data

5.2.5 Business Data

5.3 Summary

Part II Core Methods

6 Statistical Inference

6.1 Exploratory Data Analysis and Descriptive Statistics

6.1.1 Data Structure

6.1.2 Data Preprocessing

6.1.3 Summary Statistics and Presentation of Information

6.1.4 Measures of Location

6.1.4.1 Sample Mean

6.1.4.2 Trimmed Sample Mean

6.1.4.3 Sample Median

6.1.4.4 Quartile

6.1.4.5 Percentile

6.1.4.6 Mode

6.1.4.7 Proportion

6.1.5 Measures of Scale

6.1.5.1 Sample Variance

6.1.5.2 Range

6.1.5.3 Interquartile Range

6.1.6 Measures of Shape

6.1.6.1 Skewness

6.1.6.2 Kurtosis

6.1.7 Data Transformation

6.1.8 Example: Summary of Data and EDA

6.2 Sample Estimators

6.2.1 Point Estimation

6.2.2 Unbiased Estimators

6.2.3 Biased Estimators

6.2.4 Sufficiency

6.3 Bayesian Inference

6.3.1 Conjugate Priors

6.3.2 Continuous Parameter Estimation

6.3.2.1 Example: Continuous Bayesian Inference Using R

6.3.3 Discrete Parameter Estimation

6.3.4 Bayesian Credible Intervals

6.3.5 Prediction

6.3.6 Model Selection

6.4 Maximum Likelihood Estimation

6.4.1 Asymptotic Confidence Intervals for MLE

6.4.2 Bootstrap Confidence Intervals for MLE

6.4.3 Meaning of Confidence Intervals

6.5 Expectation-Maximization Algorithm

6.5.1 Example: EM Algorithm

6.6 Summary

6.7 Exercises

7 Clustering

7.1 Introduction

7.2 What Is Clustering?

7.3 Comparison of Data Points

7.3.1 Distance Measures.

7.3.2 Similarity Measures

7.4 Basic Principle of Clustering Algorithms

7.5 Non-hierarchical Clustering Methods

7.5.1 K-Means Clustering

7.5.2 K-Medoids Clustering

7.5.3 Partitioning Around Medoids (PAM)

7.6 Hierarchical Clustering

7.6.1 Dendrograms

7.6.2 Two Types of Dissimilarity Measures

7.6.3 Linkage Functions for Agglomerative Clustering

7.6.4 Example

7.7 Defining Feature Vectors for General Objects

7.8 Cluster Validation

7.8.1 External Criteria

7.8.2 Assessing the Numerical Values of Indices

7.8.3 Internal Criteria

7.9 Summary

7.10 Exercises

8 Dimension Reduction

8.1 Introduction

8.2 Feature Extraction

8.2.1 An Overview of PCA

8.2.2 Geometrical Interpretation of PCA

8.2.3 PCA Procedure

8.2.4 Underlying Mathematical Problems in PCA

8.2.5 PCA Using Singular Value Decomposition

8.2.6 Assessing PCA Results

8.2.7 Illustration of PCA Using R

8.2.8 Kernel PCA

8.2.9 Discussion

8.2.10 Non-negative Matrix Factorization

8.2.10.1 NNMF Using the Frobenius Norm as Objective Function

8.2.10.2 NNMF Using the Generalized Kullback-Leibler Divergence as Objective Function

8.2.10.3 Example of NNMF Using R

8.3 Feature Selection

8.3.1 Filter Methods Using Mutual Information

8.4 Summary

8.5 Exercises

9 Classification

9.1 Introduction

9.2 What Is Classification?

9.3 Common Aspects of Classification Methods

9.3.1 Basic Idea of a Classifier

9.3.2 Training and Test Data

9.3.3 Error Measures

9.3.3.1 Error Measures for Multi-class Classification

9.4 Naive Bayes Classifier

9.4.1 Educational Example

9.4.2 Example

9.5 Linear Discriminant Analysis

9.5.1 Extensions

9.6 Logistic Regression

9.7 k-Nearest Neighbor Classifier

9.8 Support Vector Machine

9.8.1 Linearly Separable Data

9.8.2 Nonlinearly Separable Data.

9.8.3 Nonlinear Support Vector Machines

9.8.4 Examples

9.9 Decision Tree

9.9.1 What Is a Decision Tree?

9.9.1.1 Three Principal Steps to Get a Decision Tree

9.9.2 Step 1: Growing a Decision Tree

9.9.3 Step 2: Assessing the Size of a Decision Tree

9.9.3.1 Intuitive Approach

9.9.3.2 Formal Approach

9.9.4 Step 3: Pruning a Decision Tree

9.9.4.1 Alternative Way to Construct Optimal Decision Trees: Stopping Rules

9.9.5 Predictions

9.10 Summary

9.11 Exercises

10 Hypothesis Testing

10.1 Introduction

10.2 What Is Hypothesis Testing?

10.3 Key Components of Hypothesis Testing

10.3.1 Step 1: Select Test Statistic

10.3.2 Step 2: Null Hypothesis H0 and AlternativeHypothesis H1

10.3.3 Step 3: Sampling Distribution

10.3.3.1 Examples

10.3.4 Step 4: Significance Level α

10.3.5 Step 5: Evaluate the Test Statistic from Data

10.3.6 Step 6: Determine the p-Value

10.3.7 Step 7: Make a Decision about the Null Hypothesis

10.4 Type 2 Error and Power

10.4.1 Connections between Power and Errors

10.5 Confidence Intervals

10.5.1 Confidence Intervals for a Population Mean with Known Variance

10.5.2 Confidence Intervals for a Population Mean with Unknown Variance

10.5.3 Bootstrap Confidence Intervals

10.6 Important Hypothesis Tests

10.6.1 Student's t-Test

10.6.1.1 One-Sample t-Test

10.6.1.2 Two-Sample t-Test

10.6.1.3 Extensions

10.6.2 Correlation Tests

10.6.3 Hypergeometric Test

10.6.3.1 Null Hypothesis and Sampling Distribution

10.6.3.2 Examples

10.6.4 Finding the Correct Hypothesis Test

10.7 Permutation Tests

10.8 Understanding versus Applying Hypothesis Tests

10.9 Historical Notes and Misinterpretations

10.10 Summary

10.11 Exercises

11 Linear Regression Models

11.1 Introduction

11.1.1 What Is Linear Regression?.

11.1.2 Motivating Example

11.2 Simple Linear Regression

11.2.1 Ordinary Least Squares Estimation of Coefficients

11.2.2 Variability of the Coefficients

11.2.3 Testing the Necessity of Coefficients

11.2.4 Assessing the Quality of a Fit

11.3 Preprocessing

11.4 Multiple Linear Regression

11.4.1 Testing the Necessity of Coefficients

11.4.2 Assessing the Quality of a Fit

11.5 Diagnosing Linear Models

11.5.1 Error Assumptions

11.5.2 Linearity Assumption of the Model

11.5.3 Leverage Points

11.5.4 Outliers

11.5.5 Collinearity

11.5.6 Discussion

11.6 Advanced Topics

11.6.1 Interactions

11.6.2 Nonlinearities

11.6.3 Categorical Predictors

11.6.4 Generalized Linear Models

11.6.4.1 How to Determine Which Family to Use When Fitting a GLM

11.6.4.2 Advantages of GLMs over Traditional OLS Regression

11.6.4.3 Example: Poisson Regression

11.6.4.4 Example: Logistic Regression

11.7 Summary

11.8 Exercises

12 Model Selection

12.1 Introduction

12.2 Difference Between Model Selection and Model Assessment

12.3 General Approach to Model Selection

12.4 Model Selection for Multiple Linear Regression Models

12.4.1 R2 and Adjusted R2

12.4.2 Mallow's Cp Statistic

12.4.3 Akaike's Information Criterion (AIC) and Schwarz's BIC

12.4.4 Best Subset Selection

12.4.5 Stepwise Selection

12.4.5.1 Forward Stepwise Selection

12.4.5.2 Backward Stepwise Selection

12.5 Model Selection for Generalized Linear Models

12.5.1 Negative Binomial Regression Model

12.5.2 Zero-Inflated Poisson Model

12.5.3 Quasi-Poisson Model

12.5.4 Comparison of GLMs

12.6 Model Selection for Bayesian Models

12.7 Nonparametric Model Selection for General Models with Resampling

12.8 Summary

12.9 Exercises

Part III Advanced Topics

13 Regularization

13.1 Introduction.

13.2 Preliminaries.

Notes:

Includes bibliographical references and index.

Description based on print version record.

Description based on publisher supplied metadata and other sources.

ISBN:

3-031-13339-0

OCLC:

1401961443