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Machine Learning: The Art and Science of Algorithms that Make Sense of Data

Machine Learning

The Art and Science of Algorithms that Make Sense of Data
 
Kiadás sorszáma: 9th printing
Kiadó: Cambridge University Press
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GBP 45.99
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21 102 Ft (20 097 Ft + 5% áfa)
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16 881 (16 078 Ft + 5% áfa )
Kedvezmény(ek): 20% (kb. 4 220 Ft)
A kedvezmény érvényes eddig: 2023. december 31.
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Becsült beszerzési idő: Várható beérkezés: 2024. január vége.
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Rövid leírás:

Covering all the main approaches in state-of-the-art machine learning research, this will set a new standard as an introductory textbook.

Hosszú leírás:
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.

"This textbook is clearly written and well organized. Starting from the basics, the author skillfully guides the reader through his learning process by providing useful facts and insight into the behavior of several machine learning techniques, as well as the high-level pseudocode of many key algorithms." < /br>Fernando Berzal, Computing Reviews
Tartalomjegyzék:
Prologue: a machine learning sampler; 1. The ingredients of machine learning; 2. Binary classification and related tasks; 3. Beyond binary classification; 4. Concept learning; 5. Tree models; 6. Rule models; 7. Linear models; 8. Distance
-based models; 9. Probabilistic models; 10. Features; 11. In brief: model ensembles; 12. In brief: machine learning experiments; Epilogue: where to go from here; Important points to remember; Bibliography; Index.