Artificial Intelligence for Humans, Volume 1: Fundamental Algorithms

In recent years Artificial Intelligence (AI) has rapidly gone from an obscure academic research field, to an ever more useful and ubiquitous applied discipline. We increasingly rely on AI for more and more of our everyday tasks, and whole lines of work are being thoroughly transformed by its advances.

AI’s increasing ubiquity is not making it any easier to understand. AI concepts and techniques are still domain of advanced undergraduate or graduate school level courses. There are a few popular AI books out there, but most of them don’t get “under the hood” of how AI actually works. “Artificial Intelligence for Humans, Volume 1: Fundamental Algorithms” aims to bridge that gap.

The book is very readable and relatively easy to follow. You are still expected to understand and follow basic computer algorithm and preferably be familiar with at least one programming language. This first volume in the series covers some basic AI algorithms, most of which fall under the rubric of “Machine Learning.” (ML) The book explains what learning is in the context of artificial computer systems, and explains the difference between different kinds of learning. In particular, it makes an important distinction between supervised and unsupervised learning, and explains some of the most important techniques for dealing with either one of those cases, such as regression and clustering.

The book provides a lot of examples of the techniques it covers, mostly written in pseudocode. However, I wish it provided more of the concrete examples and exercises for the reader to try on his own. I would have also appreciated a more in-depth exploration of most topics, and less coverage to the more obscure topics, such as random number generation in computers. Other than that, this is a splendid little resource for people who are new to AI and want to get an introduction to this field.

Originally published at on September 7, 2015.

One clap, two clap, three clap, forty?

By clapping more or less, you can signal to us which stories really stand out.