In recent years, machine learning has gained a lot of interest. Due to the advances in processor technology and the availability of large amounts of data, machine learning techniques have provided astounding results in areas such as object recognition or natural language processing. New approaches, e.g. deep learning, have provided groundbreaking outcomes in fields such as multimedia mining or voice recognition. Machine learning is now used in virtually every domain and deep learning algorithms are present in many devices such as smartphones, cars, drones, healthcare equipment, or smart home devices. The Internet, cloud computing and the Internet of Things produce a tsunami of data and machine learning provides the methods to effectively analyze the data and discover actionable knowledge.
This book describes the most common machine learning techniques such as Bayesian models, support vector machines, decision tree induction, regression analysis, and recurrent and convolutional neural networks. It first gives an introduction into the principles of machine learning. It then covers the basic methods including the mathematical foundations. The biggest part of the book provides common machine learning algorithms and their applications. Finally, the book gives an outlook into some of the future developments and possible new research areas of machine learning and artificial intelligence in general.
This book is meant to be an introduction into machine learning. It does not require prior knowledge in this area. It covers some of the basic mathematical principle but intends to be understandable even without a background in mathematics. It can be read chapter wise and intends to be comprehensible, even when not starting in the beginning. Finally, it also intends to be a reference book.
Key Features:
- Describes real world problems that can be solved using Machine Learning
- Provides methods for directly applying Machine Learning techniques to concrete real world problems
- Demonstrates how to apply Machine Learning techniques using different frameworks such as TensorFlow, MALLET, R

nexusstc/Machine Learning and its Applications/cd2660de478cb09c726eb5c9d5579cdf.pdf

lgrsnf/Machine Learning and Its Applications.pdf

zlib/Computers/Computer Science/Peter Wlodarczak/Machine Learning and its Applications_5558658.pdf

Wlodarczak, Peter;

Ashgate Publishing Limited

Taylor & Francis Ltd

Gower Publishing Ltd

United Kingdom and Ireland, United Kingdom

CRC Press (Unlimited), Boca Raton, 2019

First edition, Boca Raton, FL, 2020

First edition, Boca Raton, 2019

1, US, 2019

lg2537602

sources:
9781138328228

producers:
pdfTeX-1.40.18

{"edition":"1","isbns":["0429448783","042982873X","0429828748","1138328227","9780429448782","9780429828737","9780429828744","9781138328228"],"last_page":204,"publisher":"CRC Press"}

In recent years, machine learning has gained much attention. Due to the advances in processor technology and the availability of large amounts of data, machine learning techniques have provided astounding results in areas such as object recognition or natural language processing. New approaches, e.g. deep learning, have provided groundbreaking outcomes in fields such as multimedia mining or voice recognition. The book describes the most common machine learning techniques such as Bayesian models, support vector machines, decision tree induction, regression analysis, and recurrent and convolutional neural networks. It first gives an introduction into the principles of machine learning. It then covers the basic methods including the mathematical foundations. The major part of the book provides common machine learning algorithms and their applications. Finally, the book gives an outlook into some of the future developments and possible new research areas of machine learning and artificial intelligence in general This book is meant to be an introduction into machine learning. It does not require prior knowledge in this area. It covers some of the basic mathematical principle but intends to be comprehensible even without a background in mathematics. Each chapter can be read independently. It also serves a reference book.
Cover 1
Title Page 2
Copyright Page 3
Dedication 4
Preface 6
Table of Contents 10
List of Figures 14
List of Tables 16
SECTION I: INTRODUCTION 18
1: Introduction 20
1.1 Data mining 22
1.2 Data mining steps 23
1.3 Data collection 24
1.4 Data pre-processing 25
1.5 Data analysis 27
1.5.1 Supervised learning 27
1.5.2 Unsupervised learning 28
1.5.3 Semi-supervised learning 29
1.5.4 Machine learning and statistics 30
1.6 Data post-processing 32
2: Machine Learning Basics 34
2.1 Supervised learning 36
2.1.1 Perceptron 38
2.2 Unsupervised learning 42
2.2.1 k-means clustering 44
2.3 Semi-supervised learning 45
2.4 Function approximation 46
2.5 Generative and discriminative models 49
2.6 Evaluation of learner 49
2.6.1 Stochastic gradient descent 51
2.6.2 Cluster evaluation 54
SECTION Il: MACHINE LEARNING 58
3: Data Pre-processing 60
3.1 Feature extraction 61
3.2 Sampling 63
3.3 Data transformation 64
3.4 Outlier removal 64
3.5 Data deduplication 65
3.6 Relevance filtering 65
3.7 Normalization, discretization and aggregation 66
3.8 Entity resolution 67
4: Supervised Learning 70
4.1 Classification 73
4.1.1 Artificial neural networks 74
4.1.2 Bayesian models 84
4.1.3 Decision trees 86
4.1.4 Support vector machines 91
4.1.5 k-nearest neighbor 96
4.2 Regression analysis 99
4.2.1 Linear regression 102
4.2.2 Polynomial regression 108
4.3 Logistic regression 109
5: Evaluation of Learner 114
5.1 Evaluating a learner 114
5.1.1 Accuracy 116
5.1.2 Precision and recall 116
5.1.3 Confusion matrix 118
5.1.4 Receiver operating characteristic 120
6: Unsupervised Learning 124
6.1 Types of clustering 126
6.1.1 Centroid, medoid and prototype-based clustering 127
6.1.2 Density-based clustering 127
6.2 k-means clustering 127
6.3 Hierarchical clustering 130
6.4 Visualizing clusters 132
6.5 Evaluation of clusters 133
6.5.1 Silhouette coefficient 134
7: Semi-supervised Learning 136
7.1 Expectation maximization 137
7.2 Pseudo labeling 140
SECTION III: DEEP LEARNING 142
8: Deep Learning 144
8.1 Deep learning basics 145
8.1.1 Activation functions 146
8.1.2 Feature learning 149
8.2 Convolutional neural networks 150
8.3 Recurrent neural networks 154
8.4 Restricted Boltzmann machines 158
8.5 Deep belief networks 160
8.6 Deep autoencoders 161
SECTION IV: LEARNING TECHNIQUES 164
9: Learning Techniques 166
9.1 Learning issues 167
9.1.1 Bias-variance tradeoff 167
9.2 Cross-validation 171
9.3 Ensemble learning 172
9.4 Reinforcement learning 173
9.5 Active learning 174
9.6 Machine teaching 175
9.7 Automated machine learning 176
SECTION V: MACHINE LEARNING APPLICATIONS 178
10: Machine Learning Applications 180
10.1 Anomaly detection 181
10.1.1 Security 182
10.1.2 Predictive maintenance 183
10.2 Biomedicale applications 184
10.2.1 Medical applications 184
10.3 Natural language processing 185
10.3.1 Text mining 186
10.4 Other applications 188
11: Future Development 192
11.1 Research directions 194
References 198
Index 202

"This book describes Machine Learning techniques and algorithms that have been used in recent real-world application. It provides an introduction to Machine Learning, describes the most widely used techniques and methods. It also covers Deep Learning and related areas such as function approximation or. The book gives real world examples where Machine Learning techniques are applied and describes the basic math and the commonly used learning techniques"-- Provided by publisher

2020-06-06