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Probabilistic machine learning : an introduction

Probabilistic machine learning : an introduction

Material type
단행본
Personal Author
Murphy, Kevin P., 1970- author.
Title Statement
Probabilistic machine learning : an introduction / Kevin P. Murphy.
Publication, Distribution, etc
Cambridge, Massachusetts :   The MIT Press,   2022.  
Physical Medium
xxix, 826 p. : ill. (some col.) ; 24 cm.
Series Statement
Adaptive computation and machine learning series
ISBN
9780262046824
요약
"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, Etc. Note
Includes bibliographical references and index.
Subject Added Entry-Topical Term
Machine learning. Probabilities.
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040 ▼a DLC ▼b eng ▼c DLC ▼e rda ▼d DLC ▼d 211009
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100 1 ▼a Murphy, Kevin P., ▼d 1970- ▼e author.
245 1 0 ▼a Probabilistic machine learning : ▼b an introduction / ▼c Kevin P. Murphy.
260 ▼a Cambridge, Massachusetts : ▼b The MIT Press, ▼c 2022.
264 1 ▼a Cambridge, Massachusetts : ▼b The MIT Press, ▼c [2022]
300 ▼a xxix, 826 p. : ▼b ill. (some col.) ; ▼c 24 cm.
336 ▼a text ▼b txt ▼2 rdacontent
337 ▼a unmediated ▼b n ▼2 rdamedia
338 ▼a volume ▼b nc ▼2 rdacarrier
490 1 ▼a Adaptive computation and machine learning series
504 ▼a Includes bibliographical references and index.
520 ▼a "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.
650 0 ▼a Machine learning.
650 0 ▼a Probabilities.
830 0 ▼a Adaptive computation and machine learning series.
945 ▼a ITMT

Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Science & Engineering Library/Sci-Info(Stacks2)/ Call Number 006.31 M978p Accession No. 121260827 Availability Available Due Date Make a Reservation Service B M

Contents information

Table of Contents

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

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