An Introduction to Machine Learning
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Beschreibung
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.
Supplies frequent opportunities to practice techniques at the end of each chapter with control questions, exercises, thought experiments, and computer assignments Reinforces principles using well-selected toy domains and interesting real-world applications Supplementary material will be provided including an instructor's manual with PowerPoint slides Request lecturer material: sn.pub/lecturer-material
Autor*in
Miroslav Kubat
Themen in »An Introduction to Machine Learning«
Applications bayesian classifiers boosting computational learning theory decision trees genetic algorithms linear and polynomial classifiers nearest neighbor classifiers neural networks performance evaluation reinforcement learning statistical significance time-varying classes, imbalanced representation
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“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)
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