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Probabilistic Machine Learning: Advanced Topics (Adaptive Computation and Machin

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Last updated on 23 Jul, 2025 22:07:58 BSTView all revisionsView all revisions

Item specifics

Condition
Good: A book that has been read, but is in good condition. Minimal damage to the book cover eg. ...
ISBN
9780262048439

About this product

Product Identifiers

Publisher
MIT Press
ISBN-10
0262048434
ISBN-13
9780262048439
eBay Product ID (ePID)
11058354020

Product Key Features

Number of Pages
1360 Pages
Language
English
Publication Name
Probabilistic Machine Learning : Advanced Topics
Subject
Intelligence (Ai) & Semantics, Computer Science, General
Publication Year
2023
Type
Textbook
Subject Area
Computers, Science
Author
Kevin P. Murphy
Series
Adaptive Computation and Machine Learning Ser.
Format
Hardcover

Dimensions

Item Height
2.1 in
Item Weight
81.3 Oz
Item Length
9.3 in
Item Width
8.5 in

Additional Product Features

Intended Audience
Trade
LCCN
2022-045222
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
006.31015192
Table Of Content
1 Introduction 1 I Fundamentals 3 2 Probability 5 3 Statistics 63 4 Graphical models 143 5 Information theory 217 6 Optimization 255 II Inference 337 7 Inference algorithms: an overview 339 8 Gaussian filtering and smoothing 353 9 Message passing algorithms 395 10 Variational inference 433 11 Monte Carlo methods 477 12 Markov chain Monte Carlo 493 13 Sequential Monte Carlo 537 III Prediction 567 14 Predictive models: an overview 569 15 Generalized linear models 583 16 Deep neural networks 623 17 Bayesian neural networks 639 18 Gaussian processes 673 19 Beyond the iid assumption 727 IV Generation 763 20 Generative models: an overview 765 21 Variational autoencoders 781 22 Autoregressive models 811 23 Normalizing flows 819 24 Energy-based models 839 25 Diffusion models 857 26 Generative adversarial networks 883 V Discovery 915 27 Discovery methods: an overview 917 28 Latent factor models 919 29 State-space models 969 30 Graph learning 1031 31 Nonparametric Bayesian models 1035 32 Representation learning 1037 33 Interpretability 1061 VI Action 1091 34 Decision making under uncertainty 1093 35 Reinforcement learning 1133 36 Causality 1171
Synopsis
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, 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
LC Classification Number
Q325.5.M873 2023

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Half-Price-Books-Inc

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  • e***v (126)- Feedback left by buyer.
    Past 6 months
    Verified purchase
    Overall, it went well. Good communication. I wanted the book to be shipped in a box, because the value and scarcity of the volume I purchased deserved it, however, the company is too large and I was informed the book would be shipped in a plastic mailer. Somehow it didn’t arrive very damaged, a little bump, but for the price I can’t complain. Over all I am satisfied, but if you’re a book collector, as I am, you may want to find another seller.
  • s***a (766)- Feedback left by buyer.
    Past month
    Verified purchase
    This company has been, in my experience, a mixed bag. This title was NOT worth the price because it came with highlighting, ink underlineing & writing in the margins...not the quality that they advertised. I have on other occasions gotten books in great condition, however, that were listed in less than the condition advertised so it goes both ways...but I'd much prefer a company that delivers exactly what they advertise. I guess good help is hard to find. The book was well packaged & came fast.
  • a***a (14)- Feedback left by buyer.
    Past 6 months
    Verified purchase
    The shipping was quicker than I expected and book condition was exactly as advertised! Price I paid also was fair and reasonable so I could purchase. The appearance of the book showed no indication of tear or damage and book was wrapped very good and tight. I recommend this seller for his best customer service!