Probabilistic Machine Learning: Advanced Topics (Adaptive Computation and Machine Learning series)

Murphy, Kevin P.

ISBN 10: 0262048434 / ISBN 13: 9780262048439

Verlag: The MIT Press (edition ), 2023

It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting. Bestandsnummer des Verkäufers 0262048434-11-1

Inhaltsangabe:

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 

Über die Autorin bzw. den Autor: Kevin P. Murphy is a Research Scientist at Google in Mountain View, California, where he works on artificial intelligence, machine learning, and Bayesian modeling.

Bibliografische Details

Titel: Probabilistic Machine Learning: Advanced ...
Verlag: The MIT Press (edition )
Erscheinungsdatum: 2023
Einband: Hardcover
Zustand: Very Good