This book comprehensively and systematically introduces the main methods of statistical learning and is divided into two articles. The first article systematically introduces various important methods of supervised learning, including decision trees, perceptrons, support vector machines, maximum entropy models and logistic regression, boosting methods, multi-class classification methods, EM algorithms, hidden Markov models and conditional random fields, etc.; The second article introduces unsupervised learning, including clustering, singular values, principal component analysis, latent semantic analysis, etc. In the two articles, in addition to the introduction and summary, each chapter introduces one or two methods.
