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:
Contents
Preface
SECTION I: INTRODUCTION
Introduction
Data mining
Data mining steps
Data collection
Data pre-processing
Data analysis
Data post-processing
Machine learning basics
Supervised learning
Unsupervised learning
Semi-supervised learning
Function approximation
Generative and discriminative models
Evaluation of learner
SECTION II: MACHINE LEARNING
Data pre-processing
Feature extraction
Sampling
Data transformation
Outlier removal
Data deduplication
Relevance filtering
Normalization, discretization and aggregation
Entity resolution
Supervised learning
Classification
Regression analysis
Logistic regression
Evaluation of learner
Evaluating a learner
Unsupervised learning
Types of clustering
k-means clustering
Hierarchical clustering
Visualizing clusters
Evaluation of clusters
Semi-supervised learning
7.1 Expectation maximization
7.2 Pseudo labeling
SECTION III: DEEP LEARNING
Deep Learning
8.1 Deep Learning Basics
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
Learning techniques
Learning issues
Cross-validation
Ensemble learning
Reinforcement learning
Active learning
Machine teaching
Automated machine learning
SECTION V: MACHINE LEARNING APPLICATIONS
Machine Learning Applications
Anomaly detection
Biomedicale applications
Natural language processing
Other applications
Future development
Research directions
References
Index
Biography
Biography:
Peter Wlodarczak　is an IT consultant in Data Analytics and Machine Learning. Born in Basel, Switzerland, he holds a Master　degree and a PhD from the University of Southern Queensland, Australia. He has many years of experience in large software engineering and data analysis projects. He has published more than 20 papers and book chapters in this area and has presented his work on many conferences. His research interests include among other Machine Learning, eHealth and Bio computing.
