Machine learning and AI with simple Python and Matlab scripts : coursework for non-computing majors

• Main Author: Uyar, M. Ümit
• Format: Electronic eBook
• Language: English
• Published: Hoboken, New Jersey : John Wiley and Sons, Inc.; Wiley-IEEE Press, 2025
• Subjects: Machine learning.; elektronické knihy; electronic books
• ISBN: 9781394294985; 9781394294978; 9781394294961
• Physical Description: 1 online zdroj : ilustrace.

A practical guide to AI applications for Simple Python and Matlab scripts Machine Learning and AI with Simple Python and Matlab Scripts: Courseware for Non-computing Majors introduces basic concepts and principles of machine learning and artificial intelligence to help readers develop skills applica...

Table of Contents:

• About the Author xiii
• Preface xv
• Acknowledgments xvii
• About the Companion Website xix
• 1 Introduction 1
• 1.1 Artificial Intelligence 1
• 1.2 A Historical Perspective 1
• 1.3 Principles of AI 2
• 1.4 Applications That Are Impossible Without AI 2
• 1.5 Organization of This Book 3
• 2 Artificial Neural Networks 7
• 2.1 Introduction 7
• 2.2 Applications of ANNs 7
• 2.3 Components of ANNs 8
• 2.3.1 Neurons 8
• 2.3.2 Sigmoid Activation Function 9
• 2.3.3 Rectilinear Activation Function 9
• 2.3.4 Weights of Synapses 10
• 2.4 Training an ANN 11
• 2.5 Forward Propagation 12
• 2.5.1 Forward Propagation from Input to Hidden Layer 13
• 2.6 Back Propagation 13
• 2.6.1 Back Propagation for a Neuron 13
• 2.6.2 Back Propagation - from Output to Hidden Layer 15
• 2.6.3 Back Propagation - from Hidden Layer to Input 16
• 2.7 Updating Weights 17
• 2.8 ANN with Input Bias 17
• 2.9 A Simple Algorithm for ANN Training 18
• 2.10 Computational Complexity of ANN Training 18
• 2.11 Normalization of ANN Inputs and Outputs 19
• 2.12 Concluding Remarks 20
• 2.13 Exercises for Chapter 2 20
• 3 ANNs for Optimized Prediction 23
• 3.1 Introduction 23
• 3.2 Selection of ANN Inputs 24
• 3.3 Selection of ANN Outputs 24
• 3.4 Construction of Hidden Layers 25
• 3.5 Case Study 1: Sleep-Study Example 25
• 3.5.1 Using Matrices for ANN Training 26
• 3.5.2 Forward Propagation 28
• 3.5.3 Back Propagation 28
• 3.5.4 Updating Weights 29
• 3.5.5 Forward Propagation with New Weights 29
• 3.5.6 Back Propagation with New Weights 30
• 3.5.7 Using Normalized Input and Output Values 31
• 3.5.8 Reducing Errors During Training 34
• 3.5.9 Implementation of Sleep-Study ANN in Python 34
• 3.5.10 Implementation of Sleep-Study ANN in Matlab 37
• 3.6 Case Study 2: Prediction of Bike Rentals 41
• 3.6.1 Python Script for Bike Rentals Using an ANN 41
• 3.6.2 Matlab Script for Bike Rentals Using an ANN 46
• 3.7 Concluding Remarks 48
• 3.8 Exercises for Chapter 3 48
• 4 ANNs for Financial Stock Trading 51
• 4.1 Introduction 51
• 4.2 Programs that Buy and Sell Stocks 51
• 4.3 Technical Indicators 51
• 4.3.1 Simple Moving Average 52
• 4.3.2 Momentum 53
• 4.3.3 Exponential Moving Average 54
• 4.3.4 Bollinger Bands 54
• 4.4 A Simple Algorithmic Trading Policy 55
• 4.5 A Simple ANN for Algorithmic Stock Trading 57
• 4.5.1 ANN Inputs and Outputs 57
• 4.5.2 ANN Architecture 58
• 4.6 Python Script for Stock Trading Using an ANN 59
• 4.7 Matlab Script for Stock Trading Using an ANN 63
• 4.8 Concluding Remarks 65
• 4.9 Exercises for Chapter 4 65
• 5 ANNs for Alzheimer's Disease Prognosis 67
• 5.1 Introduction 67
• 5.2 Alzheimer's Disease 67
• 5.3 A Simple ANN for AD Prognosis 68
• 5.4 Python Script for AD Prognosis Using an ANN 71
• 5.5 Matlab Script for AD Prognosis Using an ANN 75
• 5.6 Concluding Remarks 80
• 5.7 Exercises for Chapter 5 81
• 6 ANNs for Natural Language Processing 83
• 6.1 Introduction 83
• 6.2 Impact of Text Messages on Stock Markets 84
• 6.3 A Simple ANN for NLP 85
• 6.3.1 ANN Inputs and Outputs 85
• 6.3.2 Keywords 85
• 6.3.3 Formation of Training Data 86
• 6.3.4 ANN Architecture 88
• 6.4 Python Script for NLP Using an ANN 89
• 6.5 Matlab Script for NLP Using an ANN 92
• 6.6 Concluding Remarks 96
• 6.7 Exercises for Chapter 6 97
• 7 Convolutional Neural Networks 99
• 7.1 Introduction 99
• 7.1.1 Training CNNs 100
• 7.2 Variations of CNNs 101
• 7.3 Applications of CNNs 101
• 7.4 CNN Components 102
• 7.5 A Numerical Example of a CNN 102
• 7.6 Computational Cost of CNN Training 108
• 7.7 Concluding Remarks 112
• 7.8 Exercises for Chapter 7 112
• 8 CNNs for Optical Character Recognition 115
• 8.1 Introduction 115
• 8.2 A Simple CNN for OCR 115
• 8.3 Organization of Training and Reference Files 117
• 8.4 Python Script for OCR Using a CNN 119
• 8.5 Matlab Script for OCR Using a CNN 124
• 8.6 Concluding Remarks 130
• 8.7 Exercises for Chapter 8 130
• 9 CNNs for Speech Recognition 133
• 9.1 Introduction 133
• 9.2 A Simple CNN for Speech Recognition 134
• 9.3 Organization of Training and Reference Files 136
• 9.4 Python Script for Speech Recognition Using a CNN 138
• 9.5 Matlab Script for Speech Recognition Using a CNN 144
• 9.6 Concluding Remarks 150
• 9.7 Exercises for Chapter 9 150
• 10 Recurrent Neural Networks 151
• 10.1 Introduction 151
• 10.2 One-to-One Single RNN Cell 153
• 10.2.1 A Simple Alphabet and One-Hot Encoding 156
• 10.2.2 Forward and Back Propagation 157
• 10.3 A Numerical Example 158
• 10.4 Multiple Hidden Layers 163
• 10.5 Embedding Layer 165
• 10.5.1 Forward and Back Propagation with Embedding 167
• 10.5.2 A Numerical Example with Embedding 168
• 10.6 Concluding Remarks 172
• 10.7 Exercises for Chapter 10 172
• 11 RNNs for Chatbot Implementation 175
• 11.1 Introduction 175
• 11.2 Many-to-Many RNN Architecture 175
• 11.3 A Simple Chatbot 176
• 11.4 Python Script for a Chatbot Using an RNN 179
• 11.5 Matlab Script for a Chatbot Using an RNN 183
• 11.6 Concluding Remarks 188
• 11.7 Exercises for Chapter 11 189
• 12 RNNs with Attention 191
• 12.1 Introduction 191
• 12.2 One-to-One RNN Cell with Attention 191
• 12.3 Forward and Back Propagation 193
• 12.4 A Numerical Example 195
• 12.5 Embedding Layer 200
• 12.6 A Numerical Example with Embedding 202
• 12.7 Concluding Remarks 207
• 12.8 Exercises for Chapter 12 207
• 13 RNNs with Attention for Machine Translation 209
• 13.1 Introduction 209
• 13.2 Many-to-Many Architecture 210
• 13.3 Python Script for Machine Translation by an RNN-Att 211
• 13.4 Matlab Script for Machine Translation by an RNN-Att 216
• 13.5 Concluding Remarks 223
• 13.6 Exercises for Chapter 13 223
• 14 Genetic Algorithms 225
• 14.1 Introduction 225
• 14.2 Genetic Algorithm Elements 226
• 14.3 A Simple Algorithm for a GA 227
• 14.4 An Example of a GA 230
• 14.5 Convergence in GAs 231
• 14.6 Concluding Remarks 232
• 14.7 Exercises for Chapter 14 232
• 15 GAs for Dietary Menu Selection 235
• 15.1 Introduction 235
• 15.2 Definition of the KP 236
• 15.3 A Simple Algorithm for the KP 238
• 15.4 Variations of the KP 239
• 15.5 GAs for KP Solution 240
• 15.6 Python Script for Dietary Menu Selection Using a GA 242
• 15.7 Matlab Script for Dietary Menu Selection Using a GA 245
• 15.8 Concluding Remarks 248
• 15.9 Exercises for Chapter 15 248
• 16 GAs for Drone Flight Control 251
• 16.1 Introduction 251
• 16.2 UAV Swarms 251
• 16.3 UAV Flight Control 252
• 16.4 A Simple GA for UAV Flight Control 253
• 16.4.1 Virtual Force-Based Fitness Function 254
• 16.4.2 FGA Progression 255
• 16.4.3 Chromosome for FGA 257
• 16.5 Python Script for UAV Flight Control Using a GA 260
• 16.6 Matlab Script for UAV Flight Control Using a GA 264
• 16.7 Concluding Remarks 270
• 16.8 Exercises for Chapter 16 271
• 17 GAs for Route Optimization 273
• 17.1 Introduction 273
• 17.2 Definition of the TSP 274
• 17.3 A Simple Algorithm for the TSP 276
• 17.4 Variations of the TSP 277
• 17.5 GA Solution for the TSP 277
• 17.6 Python Script for Route Optimization Using a GA 279
• 17.7 Matlab Script for Route Optimization Using a GA 284
• 17.8 Concluding Remarks 287
• 17.9 Exercises for Chapter 17 289
• 18 Evolutionary Methods 291
• 18.1 Introduction 291
• 18.2 Particle Swarm Optimization 291
• 18.2.1 Applications of PSO 292
• 18.2.2 PSO Operation 293
• 18.2.3 Remarks for PSO 298
• 18.3 Differential Evolution 298
• 18.3.1 Different Versions of DE 299
• 18.3.2 Applications of DE 299
• 18.3.3 A Simple Algorithm for DE 299
• 18.3.4 Numerical Example: Maximum of sinc by DE 302
• 18.3.5 Remarks for DE 305
• 18.4 Grammatical Evolution 306
• 18.4.1 A Simple Algorithm for GE 306
• 18.4.2 Definition of GE 307
• 18.4.3 A Simple GA to Implement GE 314
• 18.4.4 Remarks on GE 315
• Appendix A ANNs with Bias 317
• A.1 Introduction 317
• A.2 Training with Bias Input 317
• A.3 Forward Propagation 318
• A.3.1 Forward Propagation from Input to Hidden Layer 319
• A.3.2 Neuron Back Propagation with Bias Input 319
• Appendix B Sleep Study ANN with Bias 321
• B.1 Inclusion of Bias Term in ANN 321
• B.1.1 Inclusion of Bias in Matrices 321
• B.1.2 Forward Propagation with Biases 322
• Appendix C Back Propagation in a CNN 327
• Appendix D Back Propagation Through Time in an RNN 331
• D.1 Back Propagation in an RNN 331
• D.2 Embedding Layer 335
• Appendix E Back Propagation Through Time in an RNN with
• Attention 337
• E.1 Back Propagation in an RNN-Att 337
• E.2 Embedding Layer 340
• Bibliography 343
• Index 353.