First published in 2012, Statistical Learning Methods has been printed 25 times with 152,000 copies by April, 2019, welcomed and liked by a wide range of readers. This book comprehensively and systematically expounds on the primary methods of statistical learning in two parts. The first part gives an elaboration on the various important supervised learning methods including decision tree, perceptron, support vector machine, maximum entropy model and logistical regression, advancing method, multi-class classification, EM algorithm, Hidden Markov Model (HMM) and conditional random field, etc. The second part gives an introduction to unsupervised learning methods including clustering, singular value, principal component analysis, latent semantic analysis, etc. Except for the introduction and conclusion part, each chapter in the book presents readers with one or two statistical learning methods. Although a primer on the subject of machine learning, this book requires some basic knowledge of advanced mathematics, linear algebra, and probability statistics of readers for understanding.