Probabilistic machine learning : advanced topics / Kevin P. Murphy

Format:

Book

Author/Creator:

Murphy, Kevin P., 1970- author.

Series:

Adaptive computation and machine learning

Language:

English

Subjects (All):

Machine learning.

Probabilities.

probability.

Physical Description:

1 online resource : illustrations

illustration

Place of Publication:

Cambridge, Massachusetts : The MIT Press, [2023]

Summary:

"An advanced book for researchers and graduate students working in machine learning and statistics that reflects the influence of deep learning"-- Provided by publisher

Contents:

Probability

Statistics

Graphical models

Information theory

Optimization

Inference algorithms: an overview

Gaussian filtering and smoothing

Message passing algorithms

Variational inference

Monte Carlo methods

Markov chain Monte Carlo

Sequential Monte Carlo

Predictive models: an overview

Generalized linear models

Deep neural networks

Bayesian neural networks

Gaussian processes

Beyond the iid assumption

Generative models: an overview

Variational autoencoders

Autoregressive models

Normalizing flows

Energy-based models

Diffusion models

Generative adversarial networks

Discovery methods: an overview

Latent factor models

State-space models

Graph learning

Nonparametric Bayesian models

Representation learning

Interpretability

Decision making under uncertainty

Reinforcement learning

Causality

Notes:

Includes bibliographical references and index

Online resource; title from PDF title page (viewed September 20, 2026)

Other Format:

Print version: Murphy, Kevin P., 1970- Probabilistic machine learning

ISBN:

9780262375993

0262375990

9780262376006

0262376008

OCLC:

1359049093