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.
About the Author
Jeff Heaton is an Informatics Scientist programmer specializing in Java, C#, Groovy, Scala, C/C++, SQL, R, and Octave. He is an active technology blogger, open source contributor, and author of more than ten books. His areas of expertise include Data Science, Predictive Modeling, Data Mining, Big Data, Business Intelligence, and Artificial Intelligence. Jeff holds a Master’s Degree in Information Management from Washington University and is a Senior Member of the IEEE, a Sun Certified Java Programmer, the lead developer for the Encog Machine Learning Framework open source project, and an Associate of Reinsurance Administration (ARA, LOMA).