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Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz and Shai Ben-David Prediction, Learning, Games by Nicolo Cesa-Bianchi and Gabor Lugosi Some papers that may be of interested, related to the course, are listed here It is split into two parts: the first third dealing with a general theory of machine learning and the second two thirds applying the theory to understanding some well known ML algorithms. Understanding Machine Learning: From Theory to Algorithms, by Shai Shalev-Shwartz and Shai Ben-David 5. Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. I mean 'understanding' in quite a specific way, and this is the strength of the book. introduce machine learning, and the algorithmic paradigms it offers, in a principled way. Solution Manual and Test bank Absolute C++ (6th Ed., Walter Savitch) 3. 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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 … It is split into two parts: the first third dealing with a general theory of machine learning and the second two thirds applying the theory to understanding some well known ML algorithms. Prediction, Learning and Games, by N. Cesa-Bianchi and G. Lugosi 4. Shai Shalev-Shwartz is an Associate Professor at the School of Computer Science and Engineering at the Hebrew University of Jerusalem, Israel. Books. Understanding Machine Learning: From Theory to Algorithms, provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. 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