|Listed in category:
Have one to sell?

Probabilistic Machine Learning: An Introduction (Adaptive Computation and Mac...

Condition:
New
Price:
US $204.31
ApproximatelyEUR 189.75
Postage:
Free Economy P&P. See detailsfor postage
Located in: Chicago, Illinois, United States
Delivery:
Estimated between Thu, 9 May and Sat, 11 May to 43230
Delivery time is estimated using our proprietary method which is based on the buyer's proximity to the item location, the delivery service selected, the seller's delivery history and other factors. Delivery times may vary, especially during peak periods.
Returns:
14 days return. Buyer pays for return postage. See details- for more information about returns
Payments:
    

Shop with confidence

eBay Money Back Guarantee
Get the item you ordered or your money back. 

Seller information

Registered as a business seller
Seller assumes all responsibility for this listing.
eBay item number:296003661482
Last updated on 02 May, 2024 07:39:55 BSTView all revisionsView all revisions

Item specifics

Condition
New: A new, unread, unused book in perfect condition with no missing or damaged pages. See the ...
ISBN13
9780262046824
Book Title
Probabilistic Machine Learning: An Introduction (Adaptive Comp...
ISBN
9780262046824
Publication Name
Probabilistic Machine Learning : an Introduction
Item Length
9.3in
Publisher
MIT Press
Publication Year
2022
Series
Adaptive Computation and Machine Learning Ser.
Type
Textbook
Format
Hardcover
Language
English
Item Height
1.5in
Author
Kevin P. Murphy
Item Width
8.3in
Item Weight
55.6 Oz
Number of Pages
864 Pages

About this product

Product Information

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.

Product Identifiers

Publisher
MIT Press
ISBN-10
0262046822
ISBN-13
9780262046824
eBay Product ID (ePID)
11050020458

Product Key Features

Author
Kevin P. Murphy
Publication Name
Probabilistic Machine Learning : an Introduction
Format
Hardcover
Language
English
Publication Year
2022
Series
Adaptive Computation and Machine Learning Ser.
Type
Textbook
Number of Pages
864 Pages

Dimensions

Item Length
9.3in
Item Height
1.5in
Item Width
8.3in
Item Weight
55.6 Oz

Additional Product Features

Lc Classification Number
Q325.5.M872 2022
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
Target Audience
Trade
Topic
Computer Science, Intelligence (Ai) & Semantics, General
Lccn
2021-027430
Dewey Decimal
006.31
Dewey Edition
23
Illustrated
Yes
Genre
Computers, Science

Item description from the seller

Book Runners Store

Book Runners Store

99.5% positive Feedback
2.7K items sold

Detailed seller ratings

Average for the last 12 months

Accurate description
4.9
Reasonable postage cost
5.0
Delivery time
5.0
Communication
5.0
Registered as a business seller

Seller Feedback (406)

n***n (2268)- Feedback left by buyer.
Past month
Verified purchase
Perfect! Thank you!
1***n (56)- Feedback left by buyer.
Past month
Verified purchase
Arrived fast, better condition than expected. I would buy from again! Thank you!
s***e (642)- Feedback left by buyer.
Past month
Verified purchase
Fast ship, nice deal easy transaction