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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/50205
Ver los metadatos del registro completo
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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 |
dc.date.none.fl_str_mv |
2015-11 |
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review |
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publishedVersion |
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http://sedici.unlp.edu.ar/handle/10915/50205 |
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http://sedici.unlp.edu.ar/handle/10915/50205 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
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info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/3.0/ Creative Commons Attribution 3.0 Unported (CC BY 3.0) |
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openAccess |
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http://creativecommons.org/licenses/by/3.0/ Creative Commons Attribution 3.0 Unported (CC BY 3.0) |
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application/pdf 157-158 |
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