BIBLIO is the largest independent book marketplace in the world, with over 100 million books.

Skip to content

Artificial Intelligence for Humans, Volume 1: Fundamental Algorithms
Stock photo: cover may vary

Artificial Intelligence for Humans, Volume 1: Fundamental Algorithms Paperback - 2013

by Jeff Heaton

Add to wish list

Reader reviews for Artificial Intelligence for Humans, Volume 1: Fundamental Algorithms

From the publisher

A great building requires a strong foundation. This book teaches basic Artificial Intelligence algorithms such as dimensionality, distance metrics, clustering, error calculation, hill climbing, Nelder Mead, and linear regression. These are not just foundational algorithms for the rest of the series, but are very useful in their own right. The book explains all algorithms using actual numeric calculations that you can perform yourself. Artificial Intelligence for Humans is a book series meant to teach AI to those without an extensive mathematical background. The reader needs only a knowledge of basic college algebra or computer programming-anything more complicated than that is thoroughly explained. Every chapter also includes a programming example. Examples are currently provided in Java, C#, R, Python and C. Other languages planned.

Details

  • Title Artificial Intelligence for Humans, Volume 1: Fundamental Algorithms
  • Author Jeff Heaton
  • Binding Paperback
  • Pages 224
  • Volumes 1
  • Language ENG
  • Publisher Createspace Independent Publishing Platform
  • Publication date 2013-11-26
  • ISBN 9781493682225 / 1493682229
  • Weight 0.86 lbs (0.39 kg)
  • Dimensions 9.25 x 7.5 x 0.47 in (23.50 x 19.05 x 1.19 cm)
  • Category Computers - General Information
  • Dewey Decimal Code 006.3

About the author

Jeff Heaton, PhD, is a computer scientist that specializes in data science and artificial intelligence. Specializing in Python, R, Java and C#, he is an open source contributor and author of more than ten books. His areas of expertise include predictive modeling, data mining, big data, business intelligence, and artificial intelligence. Jeff holds a Master's Degree in Information Management from Washington University and a PhD in computer science from Nova Southeastern University in computer science. He is the lead developer for the Encog Machine Learning Framework open source project, a senior member of IEEE, and a fellow of the Life Management Institute (FLMI).