Understanding Machine Learning : From Theory to Algorithms
Shalev-Shwartz, Shai
Understanding Machine Learning : From Theory to Algorithms [Electronic Resource] / by Shai Shalev-Shwartz and Shai Ben-David. - Cambridge : Cambridge University Press, 2014 - xvi, 397p.
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
Computer Science
Machine Learning
Pattern Recognition
Machine Learning I --CSL7XX0
006.31 / Sh93U
Understanding Machine Learning : From Theory to Algorithms [Electronic Resource] / by Shai Shalev-Shwartz and Shai Ben-David. - Cambridge : Cambridge University Press, 2014 - xvi, 397p.
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
Computer Science
Machine Learning
Pattern Recognition
Machine Learning I --CSL7XX0
006.31 / Sh93U