Statistical Machine Learning will include three parts. The first part is about statistical and probabilistic foundations for machine learning and provides the fundamentals behind machine learning. The second part looks at statistical computation and inference methods such as EM algorithms, MCMC sampling, and Bootstrapping and explores the basic principles and methodology of these computational methods. The third part of this book presents machine learning models such as mixture modeless, latent data models, generalized linear models, support vector machines, online learning, randomized methods for big data and focuses on large-scale machine learning methods.

This book will provide a comprehensive description for statistical machine learning. The book will be helpful for readers from both computer science and statistics communities. Specifically, the first part is especially useful for readers from machine learning or data mining, because machine learning is built on probability and statistics and this part can fill their background in statistics and probability. The third part is very useful for readers from mathematics and statistics, because it can bring new research topics or job opportunities for them.