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OverviewFull Product DetailsAuthor: Miroslav KubatPublisher: Springer International Publishing AG Imprint: Springer International Publishing AG Edition: Softcover reprint of the original 1st ed. 2015 Dimensions: Width: 15.50cm , Height: 1.60cm , Length: 23.50cm Weight: 4.686kg ISBN: 9783319348865ISBN 10: 3319348868 Pages: 291 Publication Date: 15 October 2016 Audience: Professional and scholarly , Professional & Vocational Format: Paperback Publisher's Status: Active Availability: In Print  This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us. Table of ContentsA 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.ReviewsIt 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) 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) 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) “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) Author InformationMiroslav 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. Tab Content 6Author Website:Countries AvailableAll regions |
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