Machine Learning The Art and Science of Algorithms That Make Sense of Data
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Description: As one of the most comprehensive machine learning texts around, this book does justice to the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of increasing complexity and variety with well-chosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and state-of-the-art topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role played by features. The use of established terminology is balanced with the introduction of new and useful concepts, and summaries of relevant background material are provided with pointers for revision if necessary. These features ensure Machine Learning will set a new standard as an introductory textbook.
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All the information you need in one place! Each Study Brief is a summary of one specific subject; facts, figures, and explanations to help you learn faster.
Copyright year: 2012
Publisher: Cambridge University Press
Publication date: 9/20/2012
Size: 7.44" wide x 9.69" long x 0.71" tall
|Prologue: a machine learning sampler|
|The ingredients of machine learning|
|Binary classification and related tasks|
|Beyond binary classification|
|In brief: model ensembles|
|In brief: machine learning experiments|
|Epilogue: where to go from here|
|Important points to remember|