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
A Brief Introduction to Machine Learning for Engineersby Osvaldo Simeone - arXiv.org
This monograph provides the starting point to the literature that every engineer new to machine learning needs. It offers a basic and compact reference that describes key ideas and principles in simple terms and within a unified treatment.
(6684 views)
Practical Artificial Intelligence Programming in Javaby Mark Watson - Lulu.com
The book uses the author's libraries and the best of open source software to introduce AI (Artificial Intelligence) technologies like neural networks, genetic algorithms, expert systems, machine learning, and NLP (natural language processing).
(24568 views)
Modeling Agents with Probabilistic Programsby Owain Evans, et al. - AgentModels.org
This book describes and implements models of rational agents for (PO)MDPs and Reinforcement Learning. One motivation is to create richer models of human planning, which capture human biases. The book assumes basic programming experience.
(5714 views)
Learning Deep Architectures for AIby Yoshua Bengio - Now Publishers
This book discusses the principles of learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models such as Restricted Boltzmann Machines, used to construct deeper models.
(7805 views)