The master algorithm: how the quest for the ultimate learning machine will remake our world : Pedro Domingos. Basic Books. 2015. ISBN 978-0465065707

Autores
Hasperué, Waldo
Año de publicación
2015
Idioma
inglés
Tipo de recurso
reseña artículo
Estado
versión publicada
Descripción
Nowadays, “machine learning” is present in several aspects of the current world, internet advisors, advertisements and “smart” devices that seem to know what we need in a given moment. These are some examples of the problems solved by machine learning. This book presents the past, the present and the future of the different types of machine learning algorithms. At the beginning of the book, the author takes us to the first years of the computing science, where a programmer had to do absolutely everything by himself to make an algorithm do a certain task. As time passes, there appeared the first algorithms that were capable of programming themselves learning from the available data. The author presents what he himself calls the five “tribes” of machine learning, the essence that defends each one and the kind of problems that are able to solve without problems. With a great amount of simple examples, the author depicts which advantages and disadvantages of the “master” algorithms of each “tribes” are, saying that the problem that a tribe solves perfectly well, another one cannot do it, and the other way about. The author suggests to get the best out of each “tribe” and make a unique learning algorithm able to learn without caring about the problem: the master algorithm.
Facultad de Informática
Materia
Ciencias Informáticas
Algorithms
machine learning
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by/3.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/50205

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spelling The master algorithm: how the quest for the ultimate learning machine will remake our world : Pedro Domingos. Basic Books. 2015. ISBN 978-0465065707Hasperué, WaldoCiencias InformáticasAlgorithmsmachine learningNowadays, “machine learning” is present in several aspects of the current world, internet advisors, advertisements and “smart” devices that seem to know what we need in a given moment. These are some examples of the problems solved by machine learning. This book presents the past, the present and the future of the different types of machine learning algorithms. At the beginning of the book, the author takes us to the first years of the computing science, where a programmer had to do absolutely everything by himself to make an algorithm do a certain task. As time passes, there appeared the first algorithms that were capable of programming themselves learning from the available data. The author presents what he himself calls the five “tribes” of machine learning, the essence that defends each one and the kind of problems that are able to solve without problems. With a great amount of simple examples, the author depicts which advantages and disadvantages of the “master” algorithms of each “tribes” are, saying that the problem that a tribe solves perfectly well, another one cannot do it, and the other way about. The author suggests to get the best out of each “tribe” and make a unique learning algorithm able to learn without caring about the problem: the master algorithm.Facultad de Informática2015-11info:eu-repo/semantics/reviewinfo:eu-repo/semantics/publishedVersionRevisionhttp://purl.org/coar/resource_type/c_dcae04bcinfo:ar-repo/semantics/resenaArticuloapplication/pdf157-158http://sedici.unlp.edu.ar/handle/10915/50205enginfo:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/JCST41-BR-1.pdfinfo:eu-repo/semantics/altIdentifier/issn/1666-6038info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/3.0/Creative Commons Attribution 3.0 Unported (CC BY 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:03:55Zoai:sedici.unlp.edu.ar:10915/50205Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:03:56.027SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv The master algorithm: how the quest for the ultimate learning machine will remake our world : Pedro Domingos. Basic Books. 2015. ISBN 978-0465065707
title The master algorithm: how the quest for the ultimate learning machine will remake our world : Pedro Domingos. Basic Books. 2015. ISBN 978-0465065707
spellingShingle The master algorithm: how the quest for the ultimate learning machine will remake our world : Pedro Domingos. Basic Books. 2015. ISBN 978-0465065707
Hasperué, Waldo
Ciencias Informáticas
Algorithms
machine learning
title_short The master algorithm: how the quest for the ultimate learning machine will remake our world : Pedro Domingos. Basic Books. 2015. ISBN 978-0465065707
title_full The master algorithm: how the quest for the ultimate learning machine will remake our world : Pedro Domingos. Basic Books. 2015. ISBN 978-0465065707
title_fullStr The master algorithm: how the quest for the ultimate learning machine will remake our world : Pedro Domingos. Basic Books. 2015. ISBN 978-0465065707
title_full_unstemmed The master algorithm: how the quest for the ultimate learning machine will remake our world : Pedro Domingos. Basic Books. 2015. ISBN 978-0465065707
title_sort The master algorithm: how the quest for the ultimate learning machine will remake our world : Pedro Domingos. Basic Books. 2015. ISBN 978-0465065707
dc.creator.none.fl_str_mv Hasperué, Waldo
author Hasperué, Waldo
author_facet Hasperué, Waldo
author_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
Algorithms
machine learning
topic Ciencias Informáticas
Algorithms
machine learning
dc.description.none.fl_txt_mv Nowadays, “machine learning” is present in several aspects of the current world, internet advisors, advertisements and “smart” devices that seem to know what we need in a given moment. These are some examples of the problems solved by machine learning. This book presents the past, the present and the future of the different types of machine learning algorithms. At the beginning of the book, the author takes us to the first years of the computing science, where a programmer had to do absolutely everything by himself to make an algorithm do a certain task. As time passes, there appeared the first algorithms that were capable of programming themselves learning from the available data. The author presents what he himself calls the five “tribes” of machine learning, the essence that defends each one and the kind of problems that are able to solve without problems. With a great amount of simple examples, the author depicts which advantages and disadvantages of the “master” algorithms of each “tribes” are, saying that the problem that a tribe solves perfectly well, another one cannot do it, and the other way about. The author suggests to get the best out of each “tribe” and make a unique learning algorithm able to learn without caring about the problem: the master algorithm.
Facultad de Informática
description Nowadays, “machine learning” is present in several aspects of the current world, internet advisors, advertisements and “smart” devices that seem to know what we need in a given moment. These are some examples of the problems solved by machine learning. This book presents the past, the present and the future of the different types of machine learning algorithms. At the beginning of the book, the author takes us to the first years of the computing science, where a programmer had to do absolutely everything by himself to make an algorithm do a certain task. As time passes, there appeared the first algorithms that were capable of programming themselves learning from the available data. The author presents what he himself calls the five “tribes” of machine learning, the essence that defends each one and the kind of problems that are able to solve without problems. With a great amount of simple examples, the author depicts which advantages and disadvantages of the “master” algorithms of each “tribes” are, saying that the problem that a tribe solves perfectly well, another one cannot do it, and the other way about. The author suggests to get the best out of each “tribe” and make a unique learning algorithm able to learn without caring about the problem: the master algorithm.
publishDate 2015
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