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Probabilistic Machine Learning: Advanced Topics (Adaptive Computation and Machin
US $95.08
ApproximatelyRM 402.52
Condition:
Good
A book that has been read but is in good condition. Very minimal damage to the cover including scuff marks, but no holes or tears. The dust jacket for hard covers may not be included. Binding has minimal wear. The majority of pages are undamaged with minimal creasing or tearing, minimal pencil underlining of text, no highlighting of text, no writing in margins. No missing pages.
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Located in: Carrollton, Texas, United States
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eBay item number:336060570468
Item specifics
- Condition
- 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
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
Item description from the seller
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- i***n (73)- Feedback left by buyer.Past monthVerified purchaseOutstanding condition of the book. Super value for money. Superfast shipping. HIGHLY Recommended Seller!
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Seller feedback (38,186)
- i***n (73)- Feedback left by buyer.Past monthVerified purchaseOutstanding condition of the book. Super value for money. Superfast shipping. HIGHLY Recommended Seller!
- o***9 (2432)- Feedback left by buyer.Past monthVerified purchaseAll was smooth. No issues at all.
- n***j (241)- Feedback left by buyer.Past monthVerified purchaseAll good, thanks!