Pattern Recognition and Machine Learning

Rated 0 out of 5
Submit a Review


This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning.


  • Free Trial
  • Premium


  • Web
  • Hardcover
  • Paperback
Pattern Recognition and Machine Learning

Alternatives to Pattern Recognition and Machine Learning

Python Data Science Handbook
For many researchers, Python is a first-class tool mainly because of its libraries for storing,...
The Signal and the Noise Book for Data Science
Nate Silver built an innovative system for predicting baseball performance, predicted the 2008...
Data Science from Scratch Books for Data Science
Data science libraries, frameworks, modules, and toolkits are great for doing data science, but...
Make Your Own Neural Network, Books for Data Science
A step-by-step gentle journey through the mathematics of neural networks, and making your own using...
Storytelling with Data Books for Data Science
Storytelling with Data teaches you the fundamentals of data visualization and how to communicate...
Artificial Intelligence Books for Data Science
Artificial Intelligence: A Modern Approach (AIMA) is a university textbook on artificial...

Rate and Review for Pattern Recognition and Machine Learning

What People are Saying About Pattern Recognition and Machine Learning

There are no reviews yet. Be the first one to write one.