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| Machine learning lies on the boundary of several different academic disciplines, and its methods can be applied to diverse fields, from finance to physics. This text for undergraduate majors in computer science and engineering embraces that multidisciplinarity. Writing in a conversational tone and using familiar examples that computer science and engineering students will relate to, Marsland (Massey University, New Zealand) examines the algorithms that make up machine learning methods, and explains how and why these algorithms work. Coverage progresses from linear discriminants to the multi-layer perceptron, decision trees, ensemble learning, probability and learning, evolutionary learning, and graphical models. Worked examples, chapter problems, and b&w illustrations are included. Programming examples are supported by Python code available on a companion web site. The final chapter of the book provides a basic primer on using Python for numerical computing. Datasets for examples and exercises are drawn from the UCI Machine Learning Repository. Annotation 穢2009 Book News, Inc., Portland, OR (booknews.com) |
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