Probabilistic machine learning

Author: Murphy C. P.
Year: 2022
Number of Pages: 942

This classic work provides a modern introduction to machine learning viewed through the lens of probabilistic modeling and Bayesian decision theory. Basic mathematical apparatus (including elements of linear algebra and optimization theory), the basics of teacher-assisted learning (including linear and logistic regression and deep neural networks), and more in-depth topics (particularly transfer learning and unsupervised learning) are included.
Exercises at the end of the chapters will help readers apply what they have learned. An appendix provides a summary of the notations used.
The book will be useful for specialists in machine learning and students of specialized fields.