Personalized machine learning:
Every day we interact with machine learning systems offering individualized predictions for our entertainment, social connections, purchases, or health. These involve several modalities of data, from sequences of clicks to text, images, and social interactions. This book introduces common principles...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge, United Kingdom ; New York, NY
Cambridge University Press
2022
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| Schlagworte: | |
| Online-Zugang: | BSB01 EUV01 FHN01 TUM01 Volltext |
| Zusammenfassung: | Every day we interact with machine learning systems offering individualized predictions for our entertainment, social connections, purchases, or health. These involve several modalities of data, from sequences of clicks to text, images, and social interactions. This book introduces common principles and methods that underpin the design of personalized predictive models for a variety of settings and modalities. The book begins by revising 'traditional' machine learning models, focusing on adapting them to settings involving user data, then presents techniques based on advanced principles such as matrix factorization, deep learning, and generative modeling, and concludes with a detailed study of the consequences and risks of deploying personalized predictive systems. A series of case studies in domains ranging from e-commerce to health plus hands-on projects and code examples will give readers understanding and experience with large-scale real-world datasets and the ability to design models and systems for a wide range of applications. |
| Beschreibung: | 1 Online-Ressource (x, 326 Seiten) |
| ISBN: | 9781009003971 |
| DOI: | 10.1017/9781009003971 |
Internformat
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| discipline | Informatik |
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| isbn | 9781009003971 |
| language | English |
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| spelling | McAuley, Julian ca. 20./21. Jh. Verfasser (DE-588)1252329431 aut Personalized machine learning Julian McAuley Cambridge, United Kingdom ; New York, NY Cambridge University Press 2022 1 Online-Ressource (x, 326 Seiten) txt rdacontent c rdamedia cr rdacarrier Every day we interact with machine learning systems offering individualized predictions for our entertainment, social connections, purchases, or health. These involve several modalities of data, from sequences of clicks to text, images, and social interactions. This book introduces common principles and methods that underpin the design of personalized predictive models for a variety of settings and modalities. The book begins by revising 'traditional' machine learning models, focusing on adapting them to settings involving user data, then presents techniques based on advanced principles such as matrix factorization, deep learning, and generative modeling, and concludes with a detailed study of the consequences and risks of deploying personalized predictive systems. A series of case studies in domains ranging from e-commerce to health plus hands-on projects and code examples will give readers understanding and experience with large-scale real-world datasets and the ability to design models and systems for a wide range of applications. Machine learning Data Mining (DE-588)4428654-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Klassifikation (DE-588)4030958-7 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 s Data Mining (DE-588)4428654-5 s Klassifikation (DE-588)4030958-7 s DE-604 Erscheint auch als Druck-Ausgabe 978-1-31-651890-8 https://doi.org/10.1017/9781009003971 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | McAuley, Julian ca. 20./21. Jh Personalized machine learning Machine learning Data Mining (DE-588)4428654-5 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Klassifikation (DE-588)4030958-7 gnd |
| subject_GND | (DE-588)4428654-5 (DE-588)4193754-5 (DE-588)4030958-7 |
| title | Personalized machine learning |
| title_auth | Personalized machine learning |
| title_exact_search | Personalized machine learning |
| title_exact_search_txtP | Personalized machine learning |
| title_full | Personalized machine learning Julian McAuley |
| title_fullStr | Personalized machine learning Julian McAuley |
| title_full_unstemmed | Personalized machine learning Julian McAuley |
| title_short | Personalized machine learning |
| title_sort | personalized machine learning |
| topic | Machine learning Data Mining (DE-588)4428654-5 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Klassifikation (DE-588)4030958-7 gnd |
| topic_facet | Machine learning Data Mining Maschinelles Lernen Klassifikation |
| url | https://doi.org/10.1017/9781009003971 |
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