Probabilistic Machine Learning: An Introduction

Probabilistic Machine Learning: An Introduction

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Probabilistic Machine Learning: An Introduction

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書名

Probabilistic Machine Learning: An Introduction
確率的機械学習: 入門
著者・編者 Murphy, K.P.
発行元 The MIT Press
発行年/月 2022年2月   
装丁 Hardcover
ページ数 848 ページ
ISBN 978-0-262-04682-4
発送予定 出版後 海外倉庫よりお取り寄せ 2-3週間以内に発送します
 

Description

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.

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