From the reviews: "This book is an invaluable resource for anyone interested in exploiting the power of logical representations to learn from highly structured data. The book offers a systematic and innovative view of this important and rapidly developing area, combining technical depth and breadth of coverage. In Bristol, we use De Raedt's book as textbook for MSc students and as a reference for PhD students and researchers." (Peter A. Flach, University of Bristol) "This book provides comprehensive coverage of logical and relational learning, with an overview of inductive logic programming, multi-relational data mining, and statistical relational learning. ... The book is replete with examples, exercises, and case studies. The case studies use popular logical and relational systems and applications. The ample use of illustrations, tables, and bullet lists makes the book more readable and understandable. ... very useful to students, researchers, and practitioners in the fields of machine learning, automated knowledge discovery, data mining, and related fields." (Alexis Leon, ACM Computing Reviews, July, 2009)

• An Introduction to Logic.- An Introduction to Learning and Search.- Representations for Mining and Learning.- Generality and Logical Entailment.- The Upgrading Story.- Inducing Theories.- Probabilistic Logic Learning.- Kernels and Distances for Structured Data.- Computational Aspects of Logical and Relational Learning.- Lessons Learned.

From the reviews: "This book is an invaluable resource for anyone interested in exploiting the power of logical representations to learn from highly structured data. The book offers a systematic and innovative view of this important and rapidly developing area, combining technical depth and breadth of coverage. In Bristol, we use De Raedt's book as textbook for MSc students and as a reference for PhD students and researchers." (Peter A. Flach, University of Bristol) "This book provides comprehensive coverage of logical and relational learning, with an overview of inductive logic programming, multi-relational data mining, and statistical relational learning. ... The book is replete with examples, exercises, and case studies. The case studies use popular logical and relational systems and applications. The ample use of illustrations, tables, and bullet lists makes the book more readable and understandable. ... very useful to students, researchers, and practitioners in the fields of machine learning, automated knowledge discovery, data mining, and related fields." (Alexis Leon, ACM Computing Reviews, July, 2009)

• An Introduction to Logic.- An Introduction to Learning and Search.- Representations for Mining and Learning.- Generality and Logical Entailment.- The Upgrading Story.- Inducing Theories.- Probabilistic Logic Learning.- Kernels and Distances for Structured Data.- Computational Aspects of Logical and Relational Learning.- Lessons Learned.

From the reviews: "This book is an invaluable resource for anyone interested in exploiting the power of logical representations to learn from highly structured data. The book offers a systematic and innovative view of this important and rapidly developing area, combining technical depth and breadth of coverage. In Bristol, we use De Raedt's book as textbook for MSc students and as a reference for PhD students and researchers." (Peter A. Flach, University of Bristol) "This book provides comprehensive coverage of logical and relational learning, with an overview of inductive logic programming, multi-relational data mining, and statistical relational learning. ... The book is replete with examples, exercises, and case studies. The case studies use popular logical and relational systems and applications. The ample use of illustrations, tables, and bullet lists makes the book more readable and understandable. ... very useful to students, researchers, and practitioners in the fields of machine learning, automated knowledge discovery, data mining, and related fields." (Alexis Leon, ACM Computing Reviews, July, 2009)

• An Introduction to Logic.- An Introduction to Learning and Search.- Representations for Mining and Learning.- Generality and Logical Entailment.- The Upgrading Story.- Inducing Theories.- Probabilistic Logic Learning.- Kernels and Distances for Structured Data.- Computational Aspects of Logical and Relational Learning.- Lessons Learned.