Understanding Machine Learning: From Theory to Algorithms

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Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering.

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apteye

thanks.nice

李婷婷

看看，谢谢啊！

wangzm

好资料，好好

秦时明月sw

谢谢分享

risinglee

感謝分享!

sayangcinta

回复 1# asd223100


    Thanks for sharing

rnysun

thanks.....

心灵失格

感谢分享

gellmann

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    谢谢楼主，很好的机器学习理论书