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Probabilistic machine learning : an introduction

Probabilistic machine learning : an introduction

자료유형
단행본
개인저자
Murphy, Kevin P., 1970- author.
서명 / 저자사항
Probabilistic machine learning : an introduction / Kevin P. Murphy.
발행사항
Cambridge, Massachusetts :   The MIT Press,   2022.  
형태사항
xxix, 826 p. : ill. (some col.) ; 24 cm.
총서사항
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.
서지주기
Includes bibliographical references and index.
일반주제명
Machine learning. Probabilities.
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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

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 과학도서관/Sci-Info(2층서고)/ 청구기호 006.31 M978p 등록번호 121260827 도서상태 대출가능 반납예정일 예약 서비스 B M

컨텐츠정보

목차

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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