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An Introduction to Machine Learning, by Miroslav Kubat
Download An Introduction to Machine Learning, by Miroslav Kubat
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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.
- Sales Rank: #1191148 in Books
- Published on: 2015-07-18
- Original language: English
- Number of items: 1
- Dimensions: 9.21" h x .75" w x 6.14" l, 1.25 pounds
- Binding: Hardcover
- 291 pages
Review
“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)
About the Author
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.
Most helpful customer reviews
0 of 0 people found the following review helpful.
Loved the Book
By Amazon Customer
Kubat approaches the field of machine learning in a very mathematical yet intuitive way. The book begins with a conceptual understanding of classification through the use of Johnny's pies and continues to step-by-step build upon this knowledge through each chapter. Although there are programming challenges at the end of most chapters, no actual code is used in this book. You must be able to properly program using a mathematical basis to utilize this book to its fullest potential. Kubat smoothly teaches Machine Learning in a comprehensive and conceptual way so that you really understand the field fundamentally.
2 of 2 people found the following review helpful.
If you are going to read one book on machine learning, read this one
By Carlos
This is a must read for anyone that wants to get into machine learning. It covers all the main algorithms and practical challenges of building complex models. For a very technical book it is a very easy read, and the questions are a great way to test the understanding of the material learned. The way the material is organized is very interesting too, addressing machine learning from the multiple angles I usually look at things in real life.
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