Understanding Machine Learning : From Theory to Algorithms /
Shalev-Shwartz, Shai,
Understanding Machine Learning : From Theory to Algorithms / Shai Shalev-Shwartz, Shai Ben-David. - 1 online resource (409 pages) : digital, PDF file(s).
Title from publisher's bibliographic system (viewed on 08 Oct 2015).
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 a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed 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 advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering.
9781107298019
Machine learning
Algorithms
ebookQ325.5 / .S475 2014
Understanding Machine Learning : From Theory to Algorithms / Shai Shalev-Shwartz, Shai Ben-David. - 1 online resource (409 pages) : digital, PDF file(s).
Title from publisher's bibliographic system (viewed on 08 Oct 2015).
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 a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed 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 advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering.
9781107298019
Machine learning
Algorithms
ebookQ325.5 / .S475 2014