Understanding Machine Learning: From Theory to Algorithms

By Shai Shalev-Shwartz and Shai Ben-David

Understanding Machine Learning: From Theory to Algorithms PDF Free Download

Book Description:

Machine learning is one of the fastest growing areas of computing, with a wide variety of applications. In principle, the purpose of this manual is to introduce machine learning and the algorithmic models it proposes. The book provides a theoretical overview of the fundamentals of machine learning and the mathematical derivatives that turn these principles into practical algorithms. Following an introduction to the basic concepts, the book covers a range of fundamental topics not covered in previous textbooks. These include discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic models, including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based limits. Designed for advanced college students or recent graduates, this text makes the fundamentals and algorithms of machine learning accessible to students and non-statistical readers alike. computer science, mathematics and engineering.

 

Understanding Machine Learning: From Theory To Algorithms PDF

Author(s): Shai Shalev-Shwartz, Shai Ben-David

Publisher: Cambridge University Press, Year: 2014

ISBN: 1107057132,9781107057135

 


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