Artificial Intelligence: Pearson New International Edition

preview-18
  • Artificial Intelligence: Pearson New International Edition Book Detail

  • Author : Stuart Russell
  • Release Date : 2013-08-29
  • Publisher : Pearson Higher Ed
  • Genre : Computers
  • Pages : 1096
  • ISBN 13 : 1292037172
  • File Size : 83,83 MB

Artificial Intelligence: Pearson New International Edition by Stuart Russell PDF Summary

Book Description: For one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. The long-anticipated revision of this best-selling text offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. View chapters 3 and 4 from the Third Edition. Artificial Intelligence: A Modern Approach, 3e is available to purchase as an eText for your Kindle™, NOOK™, and the iPhone®/iPad®. You can also purchase the eText for 180 days through CourseSmart http://www.mypearsonstore.com/bookstore/product.asp?isbn=0136067336

Disclaimer: www.yourbookbest.com does not own Artificial Intelligence: Pearson New International Edition books pdf, neither created or scanned. We just provide the link that is already available on the internet, public domain and in Google Drive. If any way it violates the law or has any issues, then kindly mail us via contact us page to request the removal of the link.

Artificial Intelligence

Artificial Intelligence

File Size : 29,29 MB
Total View : 3354 Views
DOWNLOAD

Artificial Intelligence: A Modern Approach offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number

Human Compatible

Human Compatible

File Size : 72,72 MB
Total View : 3770 Views
DOWNLOAD

A leading artificial intelligence researcher lays out a new approach to AI that will enable people to coexist successfully with increasingly intelligent machine

Mathematics for Machine Learning

Mathematics for Machine Learning

File Size : 59,59 MB
Total View : 9179 Views
DOWNLOAD

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, opti