Description: Probabilistic Machine Learning : Advanced Topics, Hardcover by Murphy, Kevin P., ISBN 0262048434, ISBN-13 9780262048439, Like New Used, Free shipping in the US 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 accompanimentÂ
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Book Title: Probabilistic Machine Learning : Advanced Topics
Item Length: 9.3in
Item Height: 2.1in
Item Width: 8.5in
Author: Kevin P. Murphy
Publication Name: Probabilistic Machine Learning : Advanced Topics
Format: Hardcover
Language: English
Publisher: MIT Press
Publication Year: 2023
Series: Adaptive Computation and Machine Learning Ser.
Type: Textbook
Item Weight: 81.3 Oz
Number of Pages: 1360 Pages
