Includes bibliographical references and index.

Part I. Introduction to data mining -- What's it all about? -- Input: concepts, instances, attributes -- Output: knowledge representation -- Algorithms: the basic methods -- Credibility: evaluating what's been learned -- Preparation: data preprocessing and exploratory data analysis -- Ethics: what are the impacts of what's been learned? -- Part II. More advanced machine learning schemes -- Ensemble learning -- Extending instance-based and linear models -- Depp learning: fundamentals -- Advanced deep learning methods -- Beyond supervised and unsupervised learning -- Probabilistic methods: fundamentals -- Advanced probabilistic methods -- Moving on: applications and their consequences.