Understanding Machine Learning: From Theory to Algorithms

di Shai Shalev-Shwartz (Autore)

Shai Ben-David (Autore)

Cambridge University Press, 2014

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.

• Autore: Shai Shalev-Shwartz, Shai Ben-David
• Editore: Cambridge University Press
• Anno: 2014
• Rilegatura: Hardback
• Pagine: 410 p.
• Dimensioni: 260 x 183 mm
• Peso: 910 gr.
• EAN: 9781107057135

Testo in English