Machine and Deep Learning Using MATLAB

Algorithms and Tools for Scientists and Engineers
1. Auflage 2023
ISBN: 978-1-394-20908-8
Verlag: Wiley

Buch, Englisch, 592 Seiten, Format (B × H): 183 mm x 260 mm, Gewicht: 1292 g

MACHINE AND DEEP LEARNING

In-depth resource covering machine and deep learning methods using MATLAB tools and algorithms, providing insights and algorithmic decision-making processes

Machine and Deep Learning Using MATLAB introduces early career professionals to the power of MATLAB to explore machine and deep learning applications by explaining the relevant MATLAB tool or app and how it is used for a given method or a collection of methods. Its properties, in terms of input and output arguments, are explained, the limitations or applicability is indicated via an accompanied text or a table, and a complete running example is shown with all needed MATLAB command prompt code.

The text also presents the results, in the form of figures or tables, in parallel with the given MATLAB code, and the MATLAB written code can be later used as a template for trying to solve new cases or datasets. Throughout, the text features worked examples in each chapter for self-study with an accompanying website providing solutions and coding samples. Highlighted notes draw the attention of the user to critical points or issues.

Readers will also find information on: - Numeric data acquisition and analysis in the form of applying computational algorithms to predict the numeric data patterns (clustering or unsupervised learning)
- Relationships between predictors and response variable (supervised), categorically sub-divided into classification (discrete response) and regression (continuous response)
- Image acquisition and analysis in the form of applying one of neural networks, and estimating net accuracy, net loss, and/or RMSE for the successive training, validation, and testing steps
- Retraining and creation for image labeling, object identification, regression classification, and text recognition

Machine and Deep Learning Using MATLAB is a useful and highly comprehensive resource on the subject for professionals, advanced students, and researchers who have some familiarity with MATLAB and are situated in engineering and scientific fields, who wish to gain mastery over the software and its numerous applications.

Autoren/Hrsg.

Al-Malah, Kamal I. M.

Fachgebiete

• Technische Wissenschaften Verfahrenstechnik | Chemieingenieurwesen | Biotechnologie Chemische Verfahrenstechnik
• Technische Wissenschaften Technik Allgemein Computeranwendungen in der Technik
• Technische Wissenschaften Maschinenbau | Werkstoffkunde Maschinenbau Konstruktionslehre, Bauelemente, CAD
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz

Inhaltsverzeichnis

Preface xiii

About the Companion Website xvii

1 Unsupervised Machine Learning (ML) Techniques 1

Introduction 1

Selection of the Right Algorithm in ML 2

Classical Multidimensional Scaling of Predictors Data 2

Principal Component Analysis (PCA) 6

k-Means Clustering 13

Distance Metrics: Locations of Cluster Centroids 13

Replications 14

Gaussian Mixture Model (GMM) Clustering 15

Optimum Number of GMM Clusters 17

Observations and Clusters Visualization 18

Evaluating Cluster Quality 21

Silhouette Plots 22

Hierarchical Clustering 23

Step 1 -- Determine Hierarchical Structure 23

Step 2 -- Divide Hierarchical Tree into Clusters 25

PCA and Clustering: Wine Quality 27

Feature Selection Using Laplacian (fsulaplacian) for Unsupervised Learning 35

CHW 1.1 The Iris Flower Features Data 37

CHW 1.2 The Ionosphere Data Features 38

CHW 1.3 The Small Car Data 39

CHW 1.4 Seeds Features Data 40

2 ML Supervised Learning: Classification Models 42

Fitting Data Using Different Classification Models 42

Customizing a Model 43

Creating Training and Test Datasets 43

Predicting the Response 45

Evaluating the Classification Model 45

KNN Model for All Categorical or All Numeric Data Type 47

KNN Model: Heart Disease Numeric Data 48

Viewing the Fitting Model Properties 50

The Fitting Model: Number of Neighbors and Weighting Factor 51

The Cost Penalty of the Fitting Model 52

KNN Model: Red Wine Data 55

Using MATLAB Classification Learner 57

Binary Decision Tree Model for Multiclass Classification of All Data Types 68

Classification Tree Model: Heart Disease Numeric Data Types 70

Classification Tree Model: Heart Disease All Predictor Data Types 72

Naive Bayes Classification Model for All Data Types 74

Fitting Heart Disease Numeric Data to Naive Bayes Model 75

Fitting Heart Disease All Data Types to Naive Bayes Model 77

Discriminant Analysis (DA) Classifier for Numeric Predictors Only 79

Discriminant Analysis (DA): Heart Disease Numeric Predictors 82

Support Vector Machine (SVM) Classification Model for All Data Types 84

Properties of SVM Model 85

SVM Classification Model: Heart Disease Numeric Data Types 87

SVM Classification Model: Heart Disease All Data Types 90

Multiclass Support Vector Machine (fitcecoc) Model 92

Multiclass Support Vector Machines Model: Red Wine Data 95

Binary Linear Classifier (fitclinear) to High-Dimensional Data 98

CHW 2.1 Mushroom Edibility Data 100

CHW 2.2 1994 Adult Census Income Data 100

CHW 2.3 White Wine Classification 101

CHW 2.4 Cardiac Arrhythmia Data 102

CHW 2.5 Breast Cancer Diagnosis 102

3 Methods of Improving ML Predictive Models 103

Accuracy and Robustness of Predictive Models 103

Evaluating a Model: Cross-Validation 104

Cross-Validation Tune-up Parameters 105

Partition with K-Fold: Heart Disease Data Classification 106

Reducing Predictors: Feature Transformation and Selection 108

Factor Analysis 110

Feature Transformation and Factor Analysis: Heart Disease Data 113

Feature Selection 115

Feature Selection Using predictorImportance Function: Health Disease Data 116

Sequential Feature Selection (SFS): sequentialfs Function with Model Error Handler 118

Accommodating Categorical Data: Creating Dummy Variables 121

Feature Selection with Categorical Heart Disease Data 122

Ensemble Learning 126

Creating Ensembles: Heart Disease Data 130

Ensemble Learning: Wine Quality Classification 131

Improving fitcensemble Predictive Model: Abalone Age Prediction 132

Improving fitctree Predictive Model with Feature Selection (FS): Credit Ratings Data 134

Improving fitctree Predictive Model wit

Über Autor(innen)

Kamal I. M. Al-Malah received his PhD degree from Oregon State University in 1993. He served as a Professor of Chemical Engineering in Jordan and Gulf countries, as well as Former Chairman of the Chemical Engineering Department at the University of Hail in Saudi Arabia. Professor Al-Malah is an expert in both Aspen Plus® and MATLAB® applications. He has created a bundle of Windows-based software for engineering applications.