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Cover: 9780262046824 | Probabilistic Machine Learning | Kevin P. Murphy | Buch | Englisch
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Probabilistic Machine Learning
An Introduction
Buch von Kevin P. Murphy
Sprache: Englisch

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Beschreibung
"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"--
"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"--
Über den Autor
Kevin P. Murphy
Inhaltsverzeichnis
1 Introduction 1
I Foundations 29
2 Probability: Univariate Models 31
3 Probability: Multivariate Models 75
4 statistics 103
5 Decision Theory 163
6 Information Theory 199
7 Linear Algebra 221
8 Optimization 269
II Linear Models 315
9 Linear Discriminant Analysis 317
10 Logistic Regression 333
11 Linear Regression 365
12 Generalized Linear Models * 409
III Deep Neural Networks 417
13 Neural Networks for Structured Data 419
14 Neural Networks for Images 461
15 Neural Networks for Sequences 497
IV Nonparametric Models 539
16 Exemplar-based Methods 541
17 Kernel Methods * 561
18 Trees, Forests, Bagging, and Boosting 597
V Beyond Supervised Learning 619
19 Learning with Fewer Labeled Examples 621
20 Dimensionality Reduction 651
21 Clustering 709
22 Recommender Systems 735
23 Graph Embeddings * 747
A Notation 767
Details
Erscheinungsjahr: 2022
Genre: Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Buch
Seiten: 944
Inhalt: Einband - fest (Hardcover)
ISBN-13: 9780262046824
ISBN-10: 0262046822
Sprache: Englisch
Einband: Gebunden
Autor: Murphy, Kevin P.
Hersteller: MIT Press Ltd
Maße: 233 x 208 x 37 mm
Von/Mit: Kevin P. Murphy
Erscheinungsdatum: 01.03.2022
Gewicht: 1,558 kg
preigu-id: 120755225
Über den Autor
Kevin P. Murphy
Inhaltsverzeichnis
1 Introduction 1
I Foundations 29
2 Probability: Univariate Models 31
3 Probability: Multivariate Models 75
4 statistics 103
5 Decision Theory 163
6 Information Theory 199
7 Linear Algebra 221
8 Optimization 269
II Linear Models 315
9 Linear Discriminant Analysis 317
10 Logistic Regression 333
11 Linear Regression 365
12 Generalized Linear Models * 409
III Deep Neural Networks 417
13 Neural Networks for Structured Data 419
14 Neural Networks for Images 461
15 Neural Networks for Sequences 497
IV Nonparametric Models 539
16 Exemplar-based Methods 541
17 Kernel Methods * 561
18 Trees, Forests, Bagging, and Boosting 597
V Beyond Supervised Learning 619
19 Learning with Fewer Labeled Examples 621
20 Dimensionality Reduction 651
21 Clustering 709
22 Recommender Systems 735
23 Graph Embeddings * 747
A Notation 767
Details
Erscheinungsjahr: 2022
Genre: Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Buch
Seiten: 944
Inhalt: Einband - fest (Hardcover)
ISBN-13: 9780262046824
ISBN-10: 0262046822
Sprache: Englisch
Einband: Gebunden
Autor: Murphy, Kevin P.
Hersteller: MIT Press Ltd
Maße: 233 x 208 x 37 mm
Von/Mit: Kevin P. Murphy
Erscheinungsdatum: 01.03.2022
Gewicht: 1,558 kg
preigu-id: 120755225
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