The application of artificial intelligence : step-by-step guide from beginner to expert / Zoltán Somogyi.
Somogyi, Zoltán, author.
2021
Q335
Available Online

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Title The application of artificial intelligence : step-by-step guide from beginner to expert / Zoltán Somogyi.
Author Somogyi, Zoltán, author.
ISBN 9783030600327 (electronic bk.)
3030600327 (electronic bk.)
9783030600334 (print)
3030600335
9783030600341 (print)
3030600343
9783030600310
3030600319
DOI https://doi.org/10.1007/978-3-030-60032-7
Published Cham : Springer, [2021]
Language English
Description 1 online resource
Item Number 10.1007/978-3-030-60032-7 doi
Call Number Q335
Dewey Decimal Classification 006.3
Summary This book presents a unique, understandable view of machine learning using many practical examples and access to free professional software and open source code. The user-friendly software can immediately be used to apply everything you learn in the book without the need for programming. After an introduction to machine learning and artificial intelligence, the chapters in Part II present deeper explanations of machine learning algorithms, performance evaluation of machine learning models, and how to consider data in machine learning environments. In Part III the author explains automatic speech recognition, and in Part IV biometrics recognition, face- and speaker-recognition. By Part V the author can then explain machine learning by example, he offers cases from real-world applications, problems, and techniques, such as anomaly detection and root cause analyses, business process improvement, detecting and predicting diseases, recommendation AI, several engineering applications, predictive maintenance, automatically classifying datasets, dimensionality reduction, and image recognition. Finally, in Part VI he offers a detailed explanation of the AI-TOOLKIT, software he developed that allows the reader to test and study the examples in the book and the application of machine learning in professional environments. The author introduces core machine learning concepts and supports these with practical examples of their use, so professionals will appreciate his approach and use the book for self-study. It will also be useful as a supplementary resource for advanced undergraduate and graduate courses on machine learning and artificial intelligence.
Bibliography, etc. Note Includes bibliographical references and index.
Access Note Access limited to authorized users.
Digital File Characteristics text file
PDF
Source of Description Online resource; title from PDF title page (SpringerLink, viewed April 13, 2021).
Available in Other Form Print version: 9783030600310
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
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Intro
Preface
Support Material and Software
Acknowledgments
Contents
Abbreviations
List of Figures
List of Tables
Part I: Introduction
Chapter 1: An Introduction to Machine Learning and Artificial Intelligence (AI)
1.1 Introduction
1.2 Understanding Machine Learning
1.2.1 Accuracy and Generalization Error
1.3 Supervised Learning
1.3.1 Supervised Learning Applications
1.4 Unsupervised Learning
1.4.1 Unsupervised Learning Applications
1.5 Reinforcement Learning
1.5.1 Reinforcement Learning Applications

Part II: An In-Depth Overview of Machine Learning
Chapter 2: Machine Learning Algorithms
2.1 Introduction
2.2 Supervised Learning Algorithms
2.2.1 Support Vector Machines (SVMs)
2.2.2 Feedforward Neural Networks: Deep Learning
2.2.2.1 The Activation Function
2.2.2.2 Neural Network Layer and Connection Types
2.2.2.3 The Learning Process
2.2.2.4 How to Design a Neural Network
2.2.3 Feedforward Convolutional Neural Networks: Deep Learning
2.2.3.1 Input Layer
2.2.3.2 Convolution Layer
Filter Size (W x H)
Stride (S)
Padding (P)

Calculating the Output Size of a Convolutional layer
2.2.3.3 Pooling Layer
2.2.4 Recurrent Neural Networks
2.2.4.1 LSTM Cells
2.2.5 Random Forest (Decision Tree)
2.3 Unsupervised Learning Algorithms
2.3.1 k-Means Clustering
2.3.2 MeanShift Clustering
2.3.3 DBScan Clustering
2.3.4 Hierarchical Clustering
2.4 Reinforcement Learning Algorithms
2.4.1 Action Selection Policy: How Does the Agent Select an Action?
2.4.2 Reward Function: What Is the Cumulative Future Reward?
2.4.3 State Model: How Does the Environment Behave?

2.4.4 Example 1: Simple Business Process Automation
2.4.4.1 Actions
2.4.4.2 Design of a Reward Strategy
2.4.4.3 The Learning Process
2.4.5 Example 2: Cart-Pole (Inverted Pendulum)
2.4.5.1 Actions
2.4.5.2 Environment
2.4.5.3 Design of a Reward Strategy
2.4.5.4 The Learning Process
2.5 Hybrid Models: From Autoencoders to Deep Generative Models
2.5.1 The Autoencoder (AE)
2.5.2 The Variational Autoencoder (VAE) and Generative ML Models
2.5.3 Generative Adversarial Network (GAN)
Chapter 3: Performance Evaluation of Machine Learning Models
3.1 Introduction

3.2 Performance Measures of Supervised Learning
3.2.1 RMSE
3.2.2 The Confusion Matrix
3.2.3 Accuracy
3.2.4 Cohenś Kappa
3.2.5 Single Class Performance Measures
3.2.5.1 Precision
3.2.5.2 Recall and FNR
3.2.5.3 TNR and FPR
3.2.5.4 F1 Score
3.2.5.5 Weighted Global Performance Measures
3.2.5.6 The ROC Curve and the AUC Performance Measure
3.3 Performance Measures of Unsupervised Learning (Clustering)
3.3.1 Internal Criterion Based Performance Measures
3.3.1.1 The Silhouette Coefficient
3.3.1.2 The Calinski-Harabasz Index
3.3.1.3 The Xu-Index

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