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
Author(s): Peter Wlodarczak
Publisher: CRC Press
Year: 2020
Language: English
Pages: xvi+188
Cover
Title Page
Copyright Page
Dedication
Preface
Table of Contents
List of Figures
List of Tables
SECTION I: INTRODUCTION
1: Introduction
1.1 Data mining
1.2 Data mining steps
1.3 Data collection
1.4 Data pre-processing
1.5 Data analysis
1.5.1 Supervised learning
1.5.2 Unsupervised learning
1.5.3 Semi-supervised learning
1.5.4 Machine learning and statistics
1.6 Data post-processing
2: Machine Learning Basics
2.1 Supervised learning
2.1.1 Perceptron
2.2 Unsupervised learning
2.2.1 k-means clustering
2.3 Semi-supervised learning
2.4 Function approximation
2.5 Generative and discriminative models
2.6 Evaluation of learner
2.6.1 Stochastic gradient descent
2.6.2 Cluster evaluation
SECTION Il: MACHINE LEARNING
3: Data Pre-processing
3.1 Feature extraction
3.2 Sampling
3.3 Data transformation
3.4 Outlier removal
3.5 Data deduplication
3.6 Relevance filtering
3.7 Normalization, discretization and aggregation
3.8 Entity resolution
4: Supervised Learning
4.1 Classification
4.1.1 Artificial neural networks
4.1.2 Bayesian models
4.1.3 Decision trees
4.1.4 Support vector machines
4.1.5 k-nearest neighbor
4.2 Regression analysis
4.2.1 Linear regression
4.2.2 Polynomial regression
4.3 Logistic regression
5: Evaluation of Learner
5.1 Evaluating a learner
5.1.1 Accuracy
5.1.2 Precision and recall
5.1.3 Confusion matrix
5.1.4 Receiver operating characteristic
6: Unsupervised Learning
6.1 Types of clustering
6.1.1 Centroid, medoid and prototype-based clustering
6.1.2 Density-based clustering
6.2 k-means clustering
6.3 Hierarchical clustering
6.4 Visualizing clusters
6.5 Evaluation of clusters
6.5.1 Silhouette coefficient
7: Semi-supervised Learning
7.1 Expectation maximization
7.2 Pseudo labeling
SECTION III: DEEP LEARNING
8: Deep Learning
8.1 Deep learning basics
8.1.1 Activation functions
8.1.2 Feature learning
8.2 Convolutional neural networks
8.3 Recurrent neural networks
8.4 Restricted Boltzmann machines
8.5 Deep belief networks
8.6 Deep autoencoders
SECTION IV: LEARNING TECHNIQUES
9: Learning Techniques
9.1 Learning issues
9.1.1 Bias-variance tradeoff
9.2 Cross-validation
9.3 Ensemble learning
9.4 Reinforcement learning
9.5 Active learning
9.6 Machine teaching
9.7 Automated machine learning
SECTION V: MACHINE LEARNING APPLICATIONS
10: Machine Learning Applications
10.1 Anomaly detection
10.1.1 Security
10.1.2 Predictive maintenance
10.2 Biomedicale applications
10.2.1 Medical applications
10.3 Natural language processing
10.3.1 Text mining
10.4 Other applications
11: Future Development
11.1 Research directions
References
Index
