An Introduction to Machine Learning

Beschreibung

This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way of “boosting,” how to exploit them in more complicated domains, and how to deal with diverse advanced practical issues. One chapter is dedicated to the popular genetic algorithms.

This revised edition contains three entirely new chapters on critical topics regarding the pragmatic application of machine learning in industry. The chapters examine multi-label domains, unsupervised learning and its use in deep learning, and logical approaches to induction as well as Inductive Logic Programming. Numerous chapters have been expanded, and the presentation of the material has been enhanced. The book contains many new exercises, numerous solved examples, thought-provoking experiments, and computer assignments for independent work.

Offers frequent opportunities to practice techniques with control questions, exercises, thought experiments, and computer assignments.

Reinforces principles using well-selected toy domains and relevant real-world applications.

Provides additional material, including an instructor's manual with presentation slides, as well as answers to exercises.

Autor*in

Miroslav Kubat

Themen in »An Introduction to Machine Learning«

Bayesian classifiers boosting computational learning theory decision trees genetic algorithms linear and polynomial classifiers nearest neighbor classifier neural networks performance evaluation reinforcement learning statistical learning time-varying classes, imbalanced representation artificial intelligence machine learning data mining

Stimmen zu »An Introduction to Machine Learning«

“The presentation is mainly empirical, but precise and pedagogical, as each concept introduced is followed by a set of questions which allows the reader to check immediately whether they understand the topic. Each chapter ends with a historical summary and a series of computer assignments. … this book could serve as textbook for an undergraduate introductory course on machine learning … .” (Gilles Teyssière, Mathematical Reviews, April, 2017)

“This book describes ongoing human-computer interaction (HCI) research and practical applications. … These techniques can be very useful in AR/VR development projects, and some of these chapters can be used as examples and guides for future research.” (Miguel A. Garcia-Ruiz, Computing Reviews, January, 2019)

Details

Link teilen