Probabilistic Machine Learning : Advanced Topics

Kevin P. Murphy

ISBN 10: 0262048434 / ISBN 13: 9780262048439

Published by MIT Press Ltd Aug 2023, 2023

About this Item

Neuware - 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.

Seller Inventory # 9780262048439

Bibliographic Details

• Title: Probabilistic Machine Learning : Advanced Topics
• Publisher: MIT Press Ltd Aug 2023
• Publication Date: 2023
• Language: English
• ISBN 10: 0262048434
• ISBN 13: 9780262048439
• Binding: Buch
• Condition: Neu
• Dimensions: 233W x 215H x 52D millimeters 233 millimeters width by 215 millimeters height by 52 millimeters depth
• Weight: 2322 grams

About this title

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 

About the Author

Kevin P. Murphy is a Research Scientist at Google in Mountain View, California, where he works on artificial intelligence, machine learning, and Bayesian modeling.