Understanding machine learning : from theory to algorithms / Shai Shalev-Shwartz and Shai Ben-David
Material type: TextPublication details: India Cambridge University Press 2015Description: 397 pISBN:- 9781107512825
- 006.31 SHA-S
Item type | Current library | Collection | Shelving location | Call number | Status | Date due | Barcode | Item holds |
---|---|---|---|---|---|---|---|---|
Reference | BITS Pilani Hyderabad | 003-007 | Text & Reference Section (Student cannot borrow these books) | 006.31 SHA-S (Browse shelf(Opens below)) | Checked out | 08/08/2024 | 26107 |
"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"-
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