Probabilistic Machine Learning : Advanced Topics
Book Details
Format
Hardback or Cased Book
ISBN-10
0262048434
ISBN-13
9780262048439
Publisher
MIT Press Ltd
Imprint
MIT Press
Country of Manufacture
US
Country of Publication
GB
Publication Date
Aug 15th, 2023
Print length
1360 Pages
Weight
2,320 grams
Dimensions
21.30 x 23.70 x 5.50 cms
Product Classification:
Information technology: general issues
Ksh 26,100.00
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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.
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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