Probabilistic Machine Learning: Advanced Topics
| AUTHOR | Murphy, Kevin P.; Murphy, Kevin P. |
| PUBLISHER | MIT Press (08/15/2023) |
| PRODUCT TYPE | Hardcover (Hardcover) |
Description
An advanced book for researchers and graduate students working in machine learning and statistics who want to learn about deep learning, Bayesian inference, generative models, and decision making under uncertainty. 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
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Product Format
Product Details
ISBN-13:
9780262048439
ISBN-10:
0262048434
Binding:
Hardback or Cased Book (Sewn)
Content Language:
English
More Product Details
Page Count:
1360
Carton Quantity:
6
Product Dimensions:
8.27 x 2.05 x 9.06 inches
Weight:
5.42 pound(s)
Feature Codes:
Bibliography,
Index,
Price on Product
Country of Origin:
US
Subject Information
BISAC Categories
Computers | Data Science - Machine Learning
Computers | Computer Science
Computers | Artificial Intelligence - General
Dewey Decimal:
006.310
Library of Congress Control Number:
2022045222
Descriptions, Reviews, Etc.
publisher marketing
An advanced book for researchers and graduate students working in machine learning and statistics who want to learn about deep learning, Bayesian inference, generative models, and decision making under uncertainty. 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
Show More
Your Price
$150.00