Understanding Machine Learning: From Theory to Algorithms -- Shai Shalev-Shwartz

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.<br><br><b>Author:</b> Shai Shalev-Shwartz, Shai Ben-David<br><b>Publisher:</b> Cambridge University Press<br><b>Published:</b> 05/19/2014<br><b>Pages:</b> 410<br><b>Binding Type:</b> Hardcover<br><b>Weight:</b> 1.95lbs<br><b>Size:</b> 10.10h x 6.90w x 1.10d<br><b>ISBN:</b> 9781107057135