Download Instructor Resources of Understanding Machine Learning - Instructor's Manual
PDF Please note that by purchasing this product, you will only receive instructor resources related to the book. This product does not include the book itself. by Shai Shalev-Shwartz, Hebrew Jerusalem Shai Ben-David, Waterloo, Ontario
Information
Format: PDF Language: English Publisher: Cambridge University Press Publication Date of the Electronic Edition: 2014
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ISBN: 9781107057135, 1107057132, 9781139950619, 1139950614, 9781107298019, 9781139949569
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Description
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. Provides a principled development of the most important machine learning tools Describes a wide range of state-of-the-art algorithms Promotes understanding of when machine learning is relevant, what the prerequisites for a successful application of ML algorithms are, and which algorithms to use for any given task
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