000			01578nam a22001937a 4500
005			20260115153923.0
020			_a978-1139948517
082			_a006.31 SHW
100			_aShai Shalev-Shwartz _91224
100			_aShai Ben-David _91225
245			_aUnderstanding Machine Learning: From Theory to Algorithms
250			_a1
260			_aCambridge University Press _bNew Delhi _c2025
300			_a416 pages
520			_aMachine 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.
546			_aEnglish
650			_aCSE _914
942			_cBK
999			_c38169 _d38169