Understanding machine learning : from theory to algorithms /
"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 fundament...
| Glavni avtor: | |
|---|---|
| Drugi avtorji: | |
| Format: | Knjiga |
| Jezik: | English |
| Izdano: |
New York, NY, USA ; India :
Cambridge University Press,
2014. [Reprinted 2022]
|
| Izdaja: | First south asia edition 2015 |
| Teme: | |
| Classic Catalogue: | View this record in Classic Catalogue |
| Izvleček: | "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"-- |
|---|---|
| Fizični opis: | xvi, 397 pages : illustrations ; 26 cm. |
| Bibliografija: | Includes bibliographical references (pages 385-393) and index. |
| ISBN: | 9781107057135 (hardback) 1107057132 (hardback) 9781107512825 |