ISBN: 9780262048439
Kevin P. Murphy(Author)
Probabilistic Machine Learning: Advanced Topics (Adaptive Computation and Machine Learning series) [Hardcover] Murphy, Kevin P.
Probabilistic Machine Learning: Advanced Topics (Adaptive Computation and Machine Learning series) [Hardcover] Murphy, Kevin P.
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Author
Kevin P. Murphy(Author)
Publisher
The MIT Press
Books
1 Book
ISBN 13
9780262048439
About this edition
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 distributionsExplores how to use probabilistic models and inference for causal inference and decision makingFeatures online Python code accompaniment
More details
Publisher: The MIT Press
Language: English
Print length: 1360 pages
Binding: Print length
Dimensions: 8.39 x 2.17 x 9.29 inches
Item weight: 4.98 pounds
Best Sellers Rank (Amazon): 385166
Language: English
Print length: 1360 pages
Binding: Print length
Dimensions: 8.39 x 2.17 x 9.29 inches
Item weight: 4.98 pounds
Best Sellers Rank (Amazon): 385166
Shipping and returns
Return Policy
1. Return Window
- Eligible for return within 30 days of delivery.
35. Return Conditions
- The book must be brand new (unused, unmarked, and undamaged).
Important Notes:
If the returned book is damaged or missing components, the refund may be denied. If the book arrives damaged (e.g., due to shipping issues), a full refund will be issued. For returns due to non-quality issues (e.g., buyer’s change of mind), the customer must cover return shipping costs.
1. Return Window
- Eligible for return within 30 days of delivery.
35. Return Conditions
- The book must be brand new (unused, unmarked, and undamaged).
Important Notes:
If the returned book is damaged or missing components, the refund may be denied. If the book arrives damaged (e.g., due to shipping issues), a full refund will be issued. For returns due to non-quality issues (e.g., buyer’s change of mind), the customer must cover return shipping costs.
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The MIT Press
Probabilistic Machine Learning: Advanced Topics (Adaptive Computation and Machine Learning series) [Hardcover] Murphy, Kevin P.
What's inside the box
Book 1
Probabilistic Machine Learning
ISBN 9780262048439
About this collection
The Probabilistic Machine Learning: Advanced Topics book is an advanced textbook designed for researchers and graduate students in machine learning and statistics. It explores deep learning, Bayesian inference, generative models, graphical models, reinforcement learning, and causal reasoning. The book connects modern deep learning with probabilistic modeling and provides rigorous coverage of advanced machine learning concepts. It also includes practical Python code resources to help readers apply the techniques discussed.
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