Probabilistic Machine Learning: An Introduction

Probabilistic Machine Learning: An Introduction (Adaptive Computation and Machine Learning series)
Part of: Adaptive Computation and Machine Learning (11 Books) | Author: Kevin P. Murphy
Publisher Finelybook 出版社:The MIT Press (March 1, 2022)
Language 语言:English
pages 页数:864 pages
ISBN-10 书号:0262046822
ISBN-13 书号:9780262046824

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

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 Author: 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 Author: a sequel that covers more advanced topics, taking the same probabilistic approach.


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