**Machine Learning: A Probabilistic Perspective**

by Kevin Patrick Murphy

**Publisher**: The MIT Press 2012**ISBN-13**: 9780262018029**Number of pages**: 1098

**Description**:

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms.

Download or read it online for free here:

**Download link**

(46MB, PDF)

## Similar books

**Machine Learning for Data Streams**

by

**Albert Bifet, et al.**-

**The MIT Press**

This book presents algorithms and techniques used in data stream mining and real-time analytics. Taking a hands-on approach, the book demonstrates the techniques using MOA, allowing readers to try out the techniques after reading the explanations.

(

**6029**views)

**A Survey of Statistical Network Models**

by

**A. Goldenberg, A.X. Zheng, S.E. Fienberg, E.M. Airoldi**-

**arXiv**

We begin with the historical development of statistical network modeling and then we introduce some examples in the network literature. Our subsequent discussion focuses on prominent static and dynamic network models and their interconnections.

(

**8021**views)

**The LION Way: Machine Learning plus Intelligent Optimization**

by

**Roberto Battiti, Mauro Brunato**-

**Lionsolver, Inc.**

Learning and Intelligent Optimization (LION) is the combination of learning from data and optimization applied to solve complex problems. This book is about increasing the automation level and connecting data directly to decisions and actions.

(

**34397**views)

**Reinforcement Learning and Optimal Control**

by

**Dimitri P. Bertsekas**-

**Athena Scientific**

The book considers large and challenging multistage decision problems, which can be solved by dynamic programming and optimal control, but their exact solution is computationally intractable. We discuss solution methods that rely on approximations.

(

**8884**views)