Machine and Deep Learning Using Matlab - Algorithms and Tools for Scientists and Engineers

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.

Sommario

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
 
Accommod

Info autore

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.

Riassunto

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.

Dettagli sul prodotto

• Autori: Kamal I M Al-Malah, Kamal I. M. Al-Malah, Kamal I. M. (Oregon State University Al-Malah, Kamal I. M. (Oregon State University; University of Hail Al-Malah, Al-Malah Kamal I. M., Un
• Editore: Wiley, John and Sons Ltd
• Lingue: Inglese
• Formato: Copertina rigida
• Pubblicazione: 01.10.2023
• EAN: 9781394209088
• ISBN: 978-1-394-20908-8
• Pagine: 592
• Categorie: Scienze naturali, medicina, informatica, tecnica > Chimica; Maschinenbau, Informatik, MATLAB, Künstliche Intelligenz, KI, Maschinelles Lernen, Artificial Intelligence, Deep Learning, AI, computer science, Chemical Engineering, Mechanical Engineering, chemische Verfahrenstechnik, Computer-aided engineering, Computergestützte Verfahrenstechnik, Computational / Numerical Methods, Rechnergestützte / Numerische Verfahren im Maschinenbau