Simovici introduces mathematical analysis for machine learning and data mining to computer science students who have completed the standard sequence of calculus, linear algebra, and discrete mathematics. He covers set-theory and algebra preliminaries, topology, measure and integration, functional analysis and convexity, and applications. His topics are linear spaces, the algebra of convex sets, topology, metric space topologies, topological linear spaces, measurable spaces and measures, integration, Banach spaces, the differentiability of functions defined on normed spaces, Hilbert spaces, convex functions, optimization, iterative algorithms, neural networks, regression, and support vector machines. Annotation �2018 Ringgold, Inc., Portland, OR (protoview.com)

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