Last edited by Tagor
Sunday, August 2, 2020 | History

3 edition of Fundamentals of artificial intelligence found in the catalog.

Fundamentals of artificial intelligence

Fundamentals of artificial intelligence

an advanced course

  • 102 Want to read
  • 13 Currently reading

Published by Springer-Verlag in Berlin, New York .
Written in English


Edition Notes

Statementedited by W. Bibel and Ph. Jorrand.
ContributionsBibel, W. 1938-, Jorrand, Ph.
ID Numbers
Open LibraryOL21842137M
ISBN 103540182659

Fundamentals of Artificial Intelligence Research. Summary: This volume contains the invited and submitted contributions to FAIR '91, the first of an intended series of workshops on issues in the theoretical foundations of artificial intelligence.   Artificial Intelligence for Robotics starts with an introduction to Robot Operating Systems (ROS), Python, robotic fundamentals, and the software and tools that are required to start out with robotics. You will learn robotics concepts that will be useful for making decisions, along with basic navigation skills.

The significantly updated second edition of Fundamentals of the New Artificial Intelligence thoroughly covers the most essential and widely employed material pertaining to neural networks, genetic algorithms, fuzzy systems, rough sets, and chaos. In particular, this unique textbook explores the importance of this content for real-world. Artificial Intelligence in Practice is a fascinating look into how companies use AI and machine learning to solve problems. Presenting 50 case studies of actual situations, this book demonstrates practical applications to issues faced by businesses around the globe.

Artificial intelligence is a system that tends to simulate intelligent behaviors into computer-controlled machines or digital computers. Artificial Intelligence normally gives a machine the ability to carry out tasks usually associated with intelligent beings like us. Learn to implement basic artificial intelligence concepts with Python; Apply regression and classification concepts to real-world problems; Perform predictive analysis using decision trees and random forests; Perform clustering using the k-means and mean shift algorithms; Understand the fundamentals of deep learning via practical examples.


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