**Probabilistic Machine Learning: An Introduction by Kevin P. Murphy, ISBN-13: 978-0262046824**

[PDF eBook eTextbook]

- Publisher: The MIT Press (March 1, 2022)
- Language: English
- 864 pages
- ISBN-10: 0262046822
- ISBN-13: 978-0262046824

**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.

**Table of Contents:**

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 201

7 Linear Algebra 223

8 Optimization 269

II Linear Models 315

9 Linear Discriminant Analysis 317

10 Logistic Regression 333

11 Linear Regression 363

12 Generalized Linear Models * 405

III Deep Neural Networks 413

13 Neural Networks for Structured Data 415

14 Neural Networks for Images 457

15 Neural Networks for Sequences 493

IV Nonparametric Models 535

16 Exemplar-based Methods 537

17 Kernel Methods * 557

18 Trees, Forests, Bagging, and Boosting 593

V Beyond Supervised Learning 613

19 Learning with Fewer Labeled Examples 615

20 Dimensionality Reduction 645

21 Clustering 703

22 Recommender Systems 729

23 Graph Embeddings * 741

A Notation 761

* Kevin Patrick Murphy* was born in Ireland, grew up in England (BA from

*), and went to graduate school in the USA (MEng from U. Penn, PhD from*

**Cambridge***, Postdoc at*

**UC Berkeley***). In 2004, he became a professor of computer science and statistics at the University of British Columbia in Vancouver, Canada. In 2011, he went to Google in Mountain View, California for his sabbatical. In 2012, he converted to a full-time research scientist position at Google. Kevin has published over 50 papers in refereed conferences and journals related to machine learning and graphical models. He has recently published an 1100-page textbook called “Machine Learning: a Probabilistic Perspective”. Kevin P. Murphy is a Research Scientist at*

**Massachusetts Institute of Technology***, California, where he works on AI, machine learning, computer vision, and natural language understanding.*

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