Free shipping on all orders over $79* Limited Time Only
Track My Order
Help Centre
Likes
My Wish ListsLogin / Join
Booktopia
Australia's local bookstore
Get Free Shipping on orders over $79
Probabilistic Machine Learning : An Introduction - Kevin P. Murphy

Probabilistic Machine Learning

An Introduction

By: Kevin P. Murphy

eText | 1 March 2022

At a Glance

eText

$133.06

or 4 interest-free payments of $33.27 with

 or 

Instant online reading in your Booktopia eTextbook Library *

Why choose an eTextbook?

Instant Access *

Purchase and read your book immediately

Read Aloud

Listen and follow along as Bookshelf reads to you

Study Tools

Built-in study tools like highlights and more

* eTextbooks are not downloadable to your eReader or an app and can be accessed via web browsers only. You must be connected to the internet and have no technical issues with your device or browser that could prevent the eTextbook from operating.

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.

on
Desktop
Tablet
Mobile

You Can Find This eBook In

Non-FictionComputing & I.T.Computer ScienceArtificial IntelligenceMachine Learning

Other Editions and Formats

Hardcover

Published: 3rd October 2025

View Product

More in Artificial Intelligence

AI-Powered Search - Trey Grainger

eBOOK

AI-Powered Search

eBook

$52.99

AI : The End of Human Race - Alex Wood

eBOOK

AI

The End of Human Race

eBook

$16.99

HBR Guide to Generative AI for Managers : HBR Guide - Elisa Farri

eBOOK

HBR Guide to Generative AI for Managers

HBR Guide

eBook

RRP $37.15

$29.99

19%
OFF

This product is categorised by