Murphy, Kevin P. <br/><br/>
Probabilistic Machine Learning 

 - London  The MIT Press  2023 
 - xxviii, 1319p.  Includes index and bibliography 
<br/><br/> 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. 
<br/><br/><label>ISBN: </label>9780262048439 
<br/><br/><label>Source: </label> Textual 
<br/><br/><label>Subjects--Topical Terms: </label><br/>Machine learning<br/>Information theory<br/>Probabilities