,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
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
