Machine learning : the art and science of algorithms that make sense of data / Peter Flach.

Por: Flach, Peter ATipo de material: TextoTextoDetalles de publicación: Cambridge[etc.] : Cambridge University Press, 2012.. Descripción: xvii, 396 p. : col. ill. ; 25 cmISBN: 9781107096394 (hbk.); 1107096391 (hbk.); 9781107422223 (pbk.); 1107422221 (pbk.)Tema(s): Machine learning | Apprentissage | Sistemas de información -- Gestión | Bases de datos -- Gestión | Aprendizaje automático (Inteligencia artificial)
Contenidos:
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. Model ensembles -- 12. Machine learning experiments -- Epilogue: where to go from here.
Resumen: 'Machine Learning' brings together all the state-of-the-art methods for making sense of data. With hundreds of worked examples and explanatory figures, it explains the principles behind these methods in an intuitive yet precise manner and will appeal to novice and experienced readers alike.Covering all the main approaches in state-of-the-art machine learning research, this will set a new standard as an introductory textbook.
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Tipo de ítem Biblioteca de origen Signatura URL Estado Fecha de vencimiento Código de barras Reserva de ítems Bibliografía recomendada
Manuales 03. BIBLIOTECA INGENIERÍA PUERTO REAL
681.3/FLA/mac (Navegar estantería(Abre debajo)) Texto completo Prestado 02/04/2024 3742850666

TEORIAS DE APROXIMACION (UCA) MÁSTER EN MATEMÁTICAS Asignatura actualizada 2023-2024

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Includes bibliographical references (p. 367-381) and index.

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. Model ensembles -- 12. Machine learning experiments -- Epilogue: where to go from here.

'Machine Learning' brings together all the state-of-the-art methods for making sense of data. With hundreds of worked examples and explanatory figures, it explains the principles behind these methods in an intuitive yet precise manner and will appeal to novice and experienced readers alike.Covering all the main approaches in state-of-the-art machine learning research, this will set a new standard as an introductory textbook.

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