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 a theoretical account of the fundamentals underlying ma...
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| Format: | E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2014
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| Links: | https://doi.org/10.1017/CBO9781107298019 |
| Zusammenfassung: | 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 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. |
| Umfang: | 1 Online-Ressource (xvi, 397 Seiten) |
| ISBN: | 9781107298019 |
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| spelling | Shalev-Shwartz, Shai Understanding machine learning from theory to algorithms Shai Shalev-Shwartz, The Hebrew University, Jerusalem, Shai Ben-David, University of Waterloo, Canada Cambridge Cambridge University Press 2014 1 Online-Ressource (xvi, 397 Seiten) txt c cr 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 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. Ben-David, Shai Erscheint auch als Druck-Ausgabe 9781107057135 |
| spellingShingle | Shalev-Shwartz, Shai Understanding machine learning from theory to algorithms |
| title | Understanding machine learning from theory to algorithms |
| title_auth | Understanding machine learning from theory to algorithms |
| title_exact_search | Understanding machine learning from theory to algorithms |
| title_full | Understanding machine learning from theory to algorithms Shai Shalev-Shwartz, The Hebrew University, Jerusalem, Shai Ben-David, University of Waterloo, Canada |
| title_fullStr | Understanding machine learning from theory to algorithms Shai Shalev-Shwartz, The Hebrew University, Jerusalem, Shai Ben-David, University of Waterloo, Canada |
| title_full_unstemmed | Understanding machine learning from theory to algorithms Shai Shalev-Shwartz, The Hebrew University, Jerusalem, Shai Ben-David, University of Waterloo, Canada |
| title_short | Understanding machine learning |
| title_sort | understanding machine learning from theory to algorithms |
| title_sub | from theory to algorithms |
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