This book generally does a good job of not assuming prior math / notation knowledge. The problem I have with most ai or game theory books is that they assume you have a math undergrad or grad degree. I come from an applied arts (design) background and this book was really helpful for getting my head around the basics of ai algorithms. Some of the explanations were lacking completeness and the author doesn't clearly tie the last two chapters to the rest of the book with concrete examples. There are some formatting issues and errors in the book.

I read Artificial Intelligence for Humans, Volume 1 and then ordered volumes 2 and 3. What I like about this series is the same thing I like about Volume 2, that it's very readable. For someone without a math background, and limited programming prowess, I can understand the concepts. The only things about the book that I don't like are: 1) Some of the context is missing. For instance, I can understand Genetic Algorithms, Partical Swarm Optimization, and Ant Colony Optimization as concepts and I think I could basically code them if I needed to. I would say his forte is explaining the ideas and the math in plain language.

Jeff Heaton is a data scientist, PhD student and indy publisher. Specializing in Java, C#, C/C, Python and R, he is an active technology blogger, 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. He is also a senior member of the IEEE, a Sun-Certified Java Programmer, the lead developer for the Encog Machine Learning Framework open source project, and a fellow of the Life Management Institute (FLMI).

This book claims to be an overview of artificial intelligence, but it's not; it's an overview of machine learning. It's true that machine learning is a hot topic within AI just now, but it's hardly taken over the field, nor has it rendered all other methods obsolete. But, if you just want an informal introduction to the basic forms of machine learning, it's short and easy to read. The rubber never quite meets the road, but if all you need is the basic concepts, it's not a bad start. It does, however, contain errors that really should have been caught prior to publication. In addition to the errors mentioned by another reviewer, the references to equations 10.2 through 10.4 are wrong, and the description of the logistic function shown in Figure 10.3 doesn't match the function shown.

The book is more like a quick compilation of a college student's note. Concepts are presented in a relatively isolated manner; connections between concepts are, for the large part, missing. Furthermore, if the materials presented are rather shallow like in this book, readers will expect to see a strong emphasis on, or hands on exercises of, practical applications. But this book doesn't seem to help much in that regard either, despite what the book claims. The book does give introduction to a bunch of models, which can be useful for a beginner. But at least this edition I wouldn't suggest any one to buy because of poor editing.

All credits of the framework should go to Jeff Heaton - http://www.heatonresearch.com/encog/ The example code below will build a simple XOR Neural Network, the code is included in examples\xor-network.js This will run the same XOR example mentioned above. Should work on all Node.js Credits should go to Jeff Heaton for the original Encog Machine Learning Framework - http://www.heatonresearch.com/about/