Machine Learning and AI with Simple Python and Matlab Scripts

M. Umit Uyar 지음

Wiley-IEEE Press 2025년 03월 11일 출간

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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 applicable to many popular topics in engineering and science. Step-by-step instructions for simple Python and Matlab scripts mimicking real-life applications will enter the readers into the magical world of AI, without requiring them to have advanced math and computational skills. The book is supported by instructor only lecture slides and sample exams with multiple-choice questions. Machine Learning and AI with Simple Python and Matlab Scripts includes information on: Artificial neural networks applied to real-world problems such as algorithmic trading of financial assets, Alzheimer’s disease prognosisConvolution neural networks for speech recognition and optical character recognitionRecurrent neural networks for chatbots and natural language translatorsTypical AI tasks including flight control for autonomous drones, dietary menu planning, and route planningAdvanced AI tasks including particle swarm optimization and differential and grammatical evolution as well as the current state of the art in AI tools Machine Learning and AI with Simple Python and Matlab Scripts is an accessible, thorough, and practical learning resource for undergraduate and graduate students in engineering and science programs along with professionals in related industries seeking to expand their skill sets.

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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 Stud

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저자(글) M. Umit Uyar