01921cam a2200241 i 4500 18053648 20170107110154.0 140304s2014 nyua b 001 0 eng 9781107057135 (hardback) 1107057132 (hardback) PK-LaUMT 006.31 23 SHA-U Shalev-Shwartz, Shai Understanding machine learning : from theory to algorithms / Shai Shalev-Shwartz and Shai Ben-David New York : Cambridge University Press, 2014 xvi, 397 pages : illustrations ; 26 cm "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"-- Machine learning. Algorithms. COMPUTERS / Computer Vision & Pattern Recognition. Ben-David, Shai. BK 89907 89907 0 0 0 0 01 01 2017-01-07 5 006.31 SHA-U 104170 2026-07-04 2026-03-06 15:59:22 2026-03-06 2017-01-07 BK 0 0 0 0 01 01 2017-01-07 5 006.31 SHA-U 104171 2026-02-09 16:45:32 2025-12-31 C. 2 2017-01-07 BK