Bishop, Christopher M. <br/><br/>
Pattern recognition and machine learning 
	Christopher M Bishop 
 - New York :   Springer,   ©2006. 
 - 738 pages 
<br/><br/> 	Probability Distributions.-   Linear Models for Regression.-   Linear Models for Classification.-   Neural Networks.- .  Kernel Methods.-   Sparse Kernel Machines.-   Graphical Models.-   Mixture Models and EM.-   Approximate Inference.-   Sampling Methods.-   Continuous Latent Variables.-   Sequential Data.-   Combining Models 
<br/><br/> Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory. 
<br/><br/><label>ISBN: </label>9781493938438 
<br/><br/><label>Dewey Class. No.: </label>006.4  / BIS