Probabilistic machine learning an introduction / Kevin P. Murphy.

Author/creator Murphy, Kevin P., 1970- author.
Format Electronic
PublicationCambridge, Massachusetts : The MIT Press, [2022]
Copyright Date©2022
Description1 online resource (xxix, 826 pages) : illustrations (some color).
Supplemental ContentProQuest Ebook Central
Subjects

SeriesAdaptive computation and machine learning
Adaptive computation and machine learning. ^A474767
Contents Introduction -- Probability: univariate models -- Probability: multivariate models -- Statistics -- Decision theory -- Information theory -- Linear algebra -- Optimization -- Linear discriminant analysis -- Logistic regression -- Linear regression -- Generalized linear models -- Neural networks for structured data -- Neural networks for images -- Neural networks for sequences -- Exemplar-based methods -- Kernel methods -- Trees, forests, bagging, and boosting -- Learning with fewer labeled examples -- Dimensionality reduction -- Clustering -- Recommender systems -- Graph embeddings.
Abstract "This book provides a detailed and up-to-date coverage of machine learning. It is unique in that it unifies approaches based on deep learning with approaches based on probabilistic modeling and inference. It provides mathematical background (e.g. linear algebra, optimization), basic topics (e.g., linear and logistic regression, deep neural networks), as well as more advanced topics (e.g., Gaussian processes). It provides a perfect introduction for people who want to understand cutting edge work in top machine learning conferences such as NeurIPS, ICML and ICLR"-- Provided by publisher.
Bibliography noteIncludes bibliographical references and index.
Source of descriptionOnline resource; title from PDF title page (viewed February 14, 2022).
Issued in other formPrint version: Murphy, Kevin P., 1970- Probabilistic machine learning. Cambridge, Massachusetts : The MIT Press, [2022] 9780262046824
ISBN0262369303 (electronic book)
ISBN9780262369305 (electronic book)
ISBN0262369311 (electronic bk.)
ISBN9780262369312 (electronic bk.)
ISBN(hardcover)
ISBN(hardcover)
Stock number14260 MIT Press
Stock number9780262369312 MIT Press

Availability

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Electronic Resources Access Content Online ✔ Available