Artificial Intelligence and Machine Learning for EDGE Computing

,Table of Contents
Cover

Title page

Copyright

Contributors

Preface

Part I: AI and machine learning

Chapter 1: Supervised learning

Abstract

1: Introduction

2: Perceptron

3: Linear regression

4: Logistic regression

5: Multilayer perceptron

6: KL divergence

7: Generalized linear models

8: Kernel method

9: Nonlinear SVM classifier

10: Tree ensembles

References

Chapter 2: Supervised learning: From theory to applications

,Abstract

1: Introduction

2: What are regression and classification problems?

3: Learning algorithms

4: Evaluation metrics

5: Supervised learning to detect fraudulent credit card transactions

6: Supervised learning for hand writing recognition

7: Conclusion

References

Chapter 3: Unsupervised learning

Abstract

1: Introduction

2: k-means clustering

3: k-means++ clustering

4: Sequential leader clustering

5: EM algorithm

6: Gaussian mixture model

7: Autoencoders

8: Principal component analysis

9: Linear discriminant analysis

10: Independent component analysis

References

Chapter 4: Regression analysis

, Abstract

1: Introduction

2: Linear regression

3: Cost functions

4: Gradient descent

5: Polynomial regression

6: Regularization

7: Evaluating a machine learning model

References

Chapter 5: The integrity of machine learning algorithms against software defect prediction

Abstract

1: Introduction

2: Related works

3: Proposed method

4: Experiment

5: Results

6: Threats to validity

7: Conclusions

References

Chapter 6: Learning in sequential decision-making under uncertainty

Abstract

Acknowledgments

1: Introduction