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An advanced counterpart to Probabilistic Machine Learning: An Introduction, this high-level textbook provides researchers and graduate students detailed coverage of cutting-edge topics in machine learning, including deep generative modeling, graphical models, Bayesian inference, reinforcement learning, and causality. This volume puts deep learning into a larger statistical context and unifies approaches based on deep learning with ones based on probabilistic modeling and inference. With contributions from top scientists and domain experts from places such as Google, DeepMind, Amazon, Purdue University, NYU, and the University of Washington, this rigorous book is essential to understanding the vital issues in machine learning.
- Covers generation of high dimensional outputs, such as images, text, and graphs
- Discusses methods for discovering insights about data, based on latent variable models
- Considers training and testing under different distributions
- Explores how to use probabilistic models and inference for causal inference and decision making
- Features online Python code accompaniment
An advanced counterpart to Probabilistic Machine Learning: An Introduction, this high-level textbook provides researchers and graduate students detailed coverage of cutting-edge topics in machine learning, including deep generative modeling, graphical models, Bayesian inference, reinforcement learning, and causality. This volume puts deep learning into a larger statistical context and unifies approaches based on deep learning with ones based on probabilistic modeling and inference. With contributions from top scientists and domain experts from places such as Google, DeepMind, Amazon, Purdue University, NYU, and the University of Washington, this rigorous book is essential to understanding the vital issues in machine learning.
- Covers generation of high dimensional outputs, such as images, text, and graphs
- Discusses methods for discovering insights about data, based on latent variable models
- Considers training and testing under different distributions
- Explores how to use probabilistic models and inference for causal inference and decision making
- Features online Python code accompaniment
| Erscheinungsjahr: | 2023 |
|---|---|
| Reihe: | Adaptive Computation and Machine Learning series |
| Inhalt: | Einband - fest (Hardcover) |
| ISBN-13: | 9780262048439 |
| ISBN-10: | 0262048434 |
| Sprache: | Englisch |
| Autor: | Kevin P. Murphy |
| Hersteller: | MIT Press |
| Verantwortliche Person für die EU: | preigu, Ansas Meyer, Lengericher Landstr. 19, D-49078 Osnabrück, mail@preigu.de |
| Maße: | 240 x 210 x 60 mm |
| Von/Mit: | Kevin P. Murphy |
| Erscheinungsdatum: | 15.08.2023 |
| Gewicht: | 2,257 kg |
| Erscheinungsjahr: | 2023 |
|---|---|
| Reihe: | Adaptive Computation and Machine Learning series |
| Inhalt: | Einband - fest (Hardcover) |
| ISBN-13: | 9780262048439 |
| ISBN-10: | 0262048434 |
| Sprache: | Englisch |
| Autor: | Kevin P. Murphy |
| Hersteller: | MIT Press |
| Verantwortliche Person für die EU: | preigu, Ansas Meyer, Lengericher Landstr. 19, D-49078 Osnabrück, mail@preigu.de |
| Maße: | 240 x 210 x 60 mm |
| Von/Mit: | Kevin P. Murphy |
| Erscheinungsdatum: | 15.08.2023 |
| Gewicht: | 2,257 kg |
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