BIBLIO is the largest independent book marketplace in the world, with over 100 million books.

Skip to content

Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning series)

Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning series)

Probabilistic Machine Learning: An Introduction (Adaptive Computation and
Stock photo: cover may vary

Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning series) Hardback - 2022

by Murphy, Kevin P

Add to wish list
This seller has earned a 4.49 of 5 Stars rating from internet customers.
4.49 / 5 (77 total reviews)
  • Used
  • Good
  • Hardback
Used - Good

Description

hardcover. Good. Access codes and supplements are not guaranteed with used items. May be an ex-library book.
Ask the seller a question Add to wish list
A$208.19
Free Delivery within USA
Standard delivery: 7 to 14 days
More delivery options
Dropship order
Ships from Bonita (California, United States)
The BIBLIO Guarantee

Details

About Bonita California, United States

Biblio member since 2020

Terms of Sale: 30 day return guarantee, with full refund including original shipping costs for up to 30 days after delivery if an item arrives misdescribed or damaged.

Browse books from Bonita

Reader reviews for Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning series)

Review summary

This seller has earned a 4.49 of 5 Stars rating from internet customers.
4.49 / 5 (77 total reviews)

Readers view this probabilistic machine learning series as an ambitious, comprehensive reference work that prioritizes theoretical understanding over practical implementation. The book serves as an encyclopedic survey of modern ML topics from a Bayesian perspective, with up-to-date content and reproducible code examples. However, reviewers note it demands substantial prerequisite knowledge and features inconsistent depth across chapters, making it more suitable for experienced practitioners and researchers than beginners seeking hands-on instruction.

Readers say this book is:
comprehensive mathematical rigor encyclopedic advanced challenging uneven coverage specialized audience reference-oriented demanding prerequisites state-of-the-art

Write a review for this book

There are no reviews for this book yet. Be the first to write a review!

Your review will be anonymous unless you login or create an account first.

Overall rating
Remember: You’re rating the book as a work, not the seller or the condition of the specific copy you purchased!

Important Terms and Guidelines

  • Please focus on the book’s content and context. Also, add any personal comments as to how you enjoyed the book. Substantiate your likes and dislikes. You may make comparisons to other books.
  • Reviews must be at least 140 characters in length.
  • Please do not reveal critical plot elements.
  • This is not a help line. Contact customer support if you need help.
Your review must not include:
  • Obscenities, discriminatory language, or other insulting language not suitable for public domain
  • Advertisements, “spam” content, or references to other products, offers or websites.
  • Email addresses, URLs, phone numbers, physical addresses or other contact information.
  • Overly critical comments about other reviews or reviewers
  • Time-sensitive material (i.e. promotional tours, seminars, lectures, etc.)
  • Availability, price, or alternative ordering/shipping information

From the publisher

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.

About the author

Kevin P. Murphy is a Research Scientist at Google in Mountain View, California, where he works on AI, machine learning, computer vision, and natural language understanding.

Please wait a moment while we load the requested page.

tracking-