9783031350504 - machine learning for causal inference (19 resultados)
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Librería: Blue Vase Books, Interlochen, MI, Estados Unidos de AmericaBlue Vase Books
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Condición: good. The item shows wear from consistent use, but it remains in good condition and works perfectly. All pages and cover are intact including the dust cover, if applicable . Spine may show signs of wear. Pages may include limited notes and highlighting. May NOT include discs, access code or other supplemental material…s.
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Librería: Ria Christie Collections, Uxbridge, Reino UnidoRia Christie Collections
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Librería: Buchpark, Trebbin, AlemaniaBuchpark
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Condición: Hervorragend. Zustand: Hervorragend | Seiten: 316 | Sprache: Englisch | Produktart: Bücher | This book provides a deep understanding of the relationship between machine learning and causal inference. It covers a broad range of topics, starting with the preliminary foundations of causal inference, which include basic d…efinitions, illustrative examples, and assumptions. It then delves into the different types of classical causal inference methods, such as matching, weighting, tree-based models, and more. Additionally, the book explores how machine learning can be used for causal effect estimation based on representation learning and graph learning. The contribution of causal inference in creating trustworthy machine learning systems to accomplish diversity, non-discrimination and fairness, transparency and explainability, generalization and robustness, and more is also discussed. The book also provides practical applications of causal inference in various domains such as natural language processing, recommender systems, computer vision, time series forecasting, and continual learning. Each chapter of the book is written by leading researchers in their respective fields.Machine Learning for Causal Inference explores the challenges associated with the relationship between machine learning and causal inference, such as biased estimates of causal effects, untrustworthy models, and complicated applications in other artificial intelligence domains. However, it also presents potential solutions to these issues. The book is a valuable resource for researchers, teachers, practitioners, and students interested in these fields. It provides insights into how combining machine learning and causal inference can improve the system's capability to accomplish causal artificial intelligence based on data. The book showcases promising research directions and emphasizes the importance of understanding the causal relationship to construct different machine-learning models from data.
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Librería: Books Puddle, New York, NY, Estados Unidos de AmericaBooks Puddle
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EUR 225,94
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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EUR 171,19
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Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides a deep understanding of the relationship between machine learning and causal inference. It covers a broad range of topics, starting with the preliminary foundations of causal inference, which include basic definitions, illustrative exam…ples, and assumptions. It then delves into the different types of classical causal inference methods, such as matching, weighting, tree-based models, and more. Additionally, the book explores how machine learning can be used for causal effect estimation based on representation learning and graph learning. The contribution of causal inference in creating trustworthy machine learning systems to accomplish diversity, non-discrimination and fairness, transparency and explainability, generalization and robustness, and more is also discussed. The book also provides practical applications of causal inference in various domains such as natural language processing, recommender systems, computer vision, time series forecasting, and continual learning. Each chapter of the book is written by leading researchers in their respective fields.Machine Learning for Causal Inference explores the challenges associated with the relationship between machine learning and causal inference, such as biased estimates of causal effects, untrustworthy models, and complicated applications in other artificial intelligence domains. However, it also presents potential solutions to these issues. The book is a valuable resource for researchers, teachers, practitioners, and students interested in these fields. It provides insights into how combining machine learning and causal inference can improve the system's capability to accomplish causal artificial intelligence based on data. The book showcases promising research directions and emphasizes the importance of understanding the causal relationship to construct different machine-learning models from data.
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Librería: Revaluation Books, Exeter, Reino UnidoRevaluation Books
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EUR 250,19
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Hardcover. Condición: Brand New. 314 pages. 9.25x6.10x9.21 inches. In Stock.
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Librería: BUCHSERVICE / ANTIQUARIAT Lars Lutzer, Wahlstedt, AlemaniaBUCHSERVICE / ANTIQUARIAT Lars Lutzer
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EUR 289,90
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Hardcover. Condición: gut. 2023. Machine Learning for Causal Inference In deutscher Sprache. pages.
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Librería: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand
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EUR 134,27
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Condición: new. Questo è un articolo print on demand.
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Librería: Basi6 International, Irving, TX, Estados Unidos de AmericaBasi6 International
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EUR 144,92
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Condición: Brand New. New. Delivery takes 20-25 days. Print on Demand.
Idioma: Inglés
Editorial: Berlin Springer International Publishing Springer Nov 2023, 2023
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
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EUR 160,49
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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book provides a deep understanding of the relationship between machine learning and causal inference. It covers a broad range of topics, starting with the preliminary foundations of causal inference, which include basic definitions, i…llustrative examples, and assumptions. It then delves into the different types of classical causal inference methods, such as matching, weighting, tree-based models, and more. Additionally, the book explores how machine learning can be used for causal effect estimation based on representation learning and graph learning. The contribution of causal inference in creating trustworthy machine learning systems to accomplish diversity, non-discrimination and fairness, transparency and explainability, generalization and robustness, and more is also discussed. The book also provides practical applications of causal inference in various domains such as natural language processing, recommender systems, computer vision, time series forecasting, and continual learning. Each chapter of the book is written by leading researchers in their respective fields.Machine Learning for Causal Inference explores the challenges associated with the relationship between machine learning and causal inference, such as biased estimates of causal effects, untrustworthy models, and complicated applications in other artificial intelligence domains. However, it also presents potential solutions to these issues. The book is a valuable resource for researchers, teachers, practitioners, and students interested in these fields. It provides insights into how combining machine learning and causal inference can improve the system's capability to accomplish causal artificial intelligence based on data. The book showcases promising research directions and emphasizes the importance of understanding the causal relationship to construct different machine-learning models from data. 298 pp. Englisch.
Idioma: Inglés
Editorial: Springer, Berlin|Springer International Publishing|Springer, 2023
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Librería: moluna, Greven, Alemaniamoluna
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EUR 144,94
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Gebunden. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book provides a deep understanding of the relationship between machine learning and causal inference. It covers a broad range of topics, starting with the preliminary foundations of causal inference, wh…ich include basic definitions, illustrative exampl.
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000
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EUR 171,19
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Buch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book provides a deep understanding of the relationship between machine learning and causal inference. It covers a broad range of topics, starting with the preliminary foundations of causal inference, which include basic definitions, illus…trative examples, and assumptions. It then delves into the different types of classical causal inference methods, such as matching, weighting, tree-based models, and more. Additionally, the book explores how machine learning can be used for causal effect estimation based on representation learning and graph learning. The contribution of causal inference in creating trustworthy machine learning systems to accomplish diversity, non-discrimination and fairness, transparency and explainability, generalization and robustness, and more is also discussed. The book also provides practical applications of causal inference in various domains such as natural language processing, recommender systems, computer vision, time series forecasting, and continual learning. Each chapter of the book is written by leading researchers in their respective fields.Machine Learning for Causal Inference explores the challenges associated with the relationship between machine learning and causal inference, such as biased estimates of causal effects, untrustworthy models, and complicated applications in other artificial intelligence domains. However, it also presents potential solutions to these issues. The book is a valuable resource for researchers, teachers, practitioners, and students interested in these fields. It provides insights into how combining machine learning and causal inference can improve the system's capability to accomplish causal artificial intelligence based on data. The book showcases promising research directions and emphasizes the importance of understanding the causal relationship to construct different machine-learning models from data.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 316 pp. Englisch.
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Librería: Majestic Books, Hounslow, Reino UnidoMajestic Books
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EUR 236,82
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Librería: Biblios, frankfurt am main, HESSE, AlemaniaBiblios
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EUR 240,95
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