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Probabilistic Machine Learning: An Introduction (Adaptive Computation Like New
US $42.95
ApproximatelyPHP 2,382.18
Condition:
Like New
A book in excellent condition. Cover is shiny and undamaged, and the dust jacket is included for hard covers. No missing or damaged pages, no creases or tears, and no underlining/highlighting of text or writing in the margins. May be very minimal identifying marks on the inside cover. Very minimal wear and tear.
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US $7.96 (approx PHP 441.49) USPS Ground Advantage®.
Located in: Brownsville, Texas, United States
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eBay item number:226741610671
Item specifics
- Condition
- Brand
- Unbranded
- Book Title
- Probabilistic Machine Learning: An Introduction (Adaptive Comput
- MPN
- Does not apply
- ISBN
- 9780262046824
About this product
Product Identifiers
Publisher
MIT Press
ISBN-10
0262046822
ISBN-13
9780262046824
eBay Product ID (ePID)
11050020458
Product Key Features
Number of Pages
864 Pages
Publication Name
Probabilistic Machine Learning : an Introduction
Language
English
Subject
Intelligence (Ai) & Semantics, Computer Science, General
Publication Year
2022
Type
Textbook
Subject Area
Computers, Science
Series
Adaptive Computation and Machine Learning Ser.
Format
Hardcover
Dimensions
Item Height
1.5 in
Item Weight
55.6 Oz
Item Length
9.3 in
Item Width
8.3 in
Additional Product Features
Intended Audience
Trade
LCCN
2021-027430
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
006.31
Table Of Content
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
Synopsis
A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation. Probabilistic Machine Learning grew out of the author's 2012 book, Machine Learning- A Probabilistic Perspective . More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the new book is accompanied by online Python code, using libraries such as scikit-learn, JAX, PyTorch, and Tensorflow, which can be used to reproduce nearly all the figures; this code can be run inside a web browser using cloud-based notebooks, and provides a practical complement to the theoretical topics discussed in the book. This introductory text will be followed by a sequel that covers more advanced topics, taking the same probabilistic approach., A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation. Probabilistic Machine Learning grew out of the author's 2012 book, Machine Learning: A Probabilistic Perspective . More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the new book is accompanied by online Python code, using libraries such as scikit-learn, JAX, PyTorch, and Tensorflow, which can be used to reproduce nearly all the figures; this code can be run inside a web browser using cloud-based notebooks, and provides a practical complement to the theoretical topics discussed in the book. This introductory text will be followed by a sequel that covers more advanced topics, taking the same probabilistic approach.
LC Classification Number
Q325.5.M872 2022
Item description from the seller
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Seller feedback (737)
- t***0 (67)- Feedback left by buyer.Past monthVerified purchaseArrived on time if not early husband is very pleased with item
- r***p (8)- Feedback left by buyer.Past monthVerified purchaseItem arrives quickly and as advertised. Thank you!
- t***. (823)- Feedback left by buyer.Past monthVerified purchase👍