An Introduction to Machine Learning

Miroslav Kubat (Autor)

Buch | Softcover

XIII, 291 Seiten

2016 | 1. Softcover reprint of the original 1st ed. 2015
Springer International Publishing (Verlag)
978-3-319-34886-5 (ISBN)

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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. 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.

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.

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.

"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)

Erscheinungsdatum	21.10.2016
Zusatzinfo	XIII, 291 p. 71 illus., 2 illus. in color.
Verlagsort	Cham
Sprache	englisch
Maße	155 x 235 mm
Themenwelt	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
Schlagworte	3D graphics and modelling • Applications • Artificial Intelligence • artificial intelligence (incl. robotics) • bayesian classifiers • Boosting • Computational Learning Theory • Computer Science • Data Warehousing • decision trees • Genetic algorithms • Information Retrieval • Information storage and retrieval • linear and polynomial classifiers • nearest neighbor classifiers • Neural networks • pattern recognition • Performance Evaluation • Reinforcement Learning • Robotics • Simulation and modeling • statistical significance • time-varying classes, imbalanced representation
ISBN-10	3-319-34886-8 / 3319348868
ISBN-13	978-3-319-34886-5 / 9783319348865
Zustand	Neuware