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Understanding Machine Learning
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AED 323.00
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AED 265.00
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.
Publisher Name | Cambridge University Press |
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Author Name | Hagendorf, Col |
Format | Audio |
Bisac Subject Major | COM |
Language | NG |
Isbn 10 | 1107057132 |
Isbn 13 | 9781107057135 |
Target Age Group | min:NA, max:NA |
Dimensions | 01.01" H x 00.06" L x 90.00" W |
Page Count | 410 |
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