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An Introduction to Machine Learning

Previously published in hardcover
BuchKartoniert, Paperback
291 Seiten
Englisch
Springererschienen am15.10.2016Softcover reprint of the original 1st ed. 2015
This book presents basic ideas of machine learning in a way that is easy to understand, by providing hands-on practical advice, using simple examples, and motivating students with discussions of interesting applications.mehr
Verfügbare Formate
BuchKartoniert, Paperback
EUR53,49
BuchKartoniert, Paperback
EUR69,54
BuchGebunden
EUR64,19
E-BookPDF1 - PDF WatermarkE-Book
EUR53,49
E-BookPDF1 - PDF WatermarkE-Book
EUR53,49
E-BookPDF1 - PDF WatermarkE-Book
EUR64,19

Produkt

KlappentextThis book presents basic ideas of machine learning in a way that is easy to understand, by providing hands-on practical advice, using simple examples, and motivating students with discussions of interesting applications.
Details
ISBN/GTIN978-3-319-34886-5
ProduktartBuch
EinbandartKartoniert, Paperback
Verlag
Erscheinungsjahr2016
Erscheinungsdatum15.10.2016
AuflageSoftcover reprint of the original 1st ed. 2015
Seiten291 Seiten
SpracheEnglisch
Gewicht474 g
IllustrationenXIII, 291 p. 71 illus., 2 illus. in color.
Artikel-Nr.40916397

Inhalt/Kritik

Inhaltsverzeichnis
A Simple Machine-Learning Task.- Probabilities: Bayesian Classifiers.- Similarities: Nearest-Neighbor Classifiers.- Inter-Class Boundaries: Linear and Polynomial Classifiers.- Artificial Neural Networks.- Decision Trees.- Computational Learning Theory.- A Few Instructive Applications.- Induction of Voting Assemblies.- Some Practical Aspects to Know About.- Performance Evaluation.-Statistical Significance.- The Genetic Algorithm.- Reinforcement learning.mehr
Kritik
"Miroslav Kubat's Introduction to Machine Learning is an excellent overview of a broad range of Machine Learning (ML) techniques. It fills a longstanding need for texts that cover the middle ground of neither oversimplifying nor too technical explanations of key concepts of key Machine Learning algorithms. ... All in all it is a very informative and instructive read which is well suited for undergraduate students and aspiring data scientists." (Holger K. von Joua, Google+, plus.google.com, December, 2016)

"It is superbly organized: each section includes a 'what have you learned' summary, and every chapter has a short summary, accompanying (brief) historical remarks, and a slew of exercises. ... In most of the chapters, there are very clear examples, well chosen and illustrated, that really help the reader understand each concept. ... I did learn quite a bit about very basic machine learning by reading this book." (Jacques Carette, Computing Reviews, January, 2016)mehr

Schlagworte

Autor

Miroslav Kubat, Associate Professor at the University of Miami, has been teaching and studying machine learning for more than a quarter century. Over the years, he has published more than 100 peer-reviewed papers, co-edited two books, served on the program committees of some 60 program conferences and workshops, and is the member of the editorial boards of three scientific journals. He is widely credited for having co-pioneered research in two major branches of the discipline: induction of time-varying concepts and learning from imbalanced training sets. Apart from that, he contributed to induction from multi-label examples, induction of hierarchically organized classes, genetic algorithms, initialization of neural networks, and other problems.