Despite the current popularity of machine learning, I haven’t found any short introductions to it which quite match the way I prefer to introduce people to the field. So here’s my own. Compared with other introductions, I’ve focused less on explaining each concept in detail, and more on explaining how they relate to other important concepts in AI, especially in diagram form. If you're new to machine learning, you shouldn't expect to fully understand most of the concepts explained here just after reading this post - the goal is instead to provide a broad framework which will contextualise more detailed explanations you'll receive from elsewhere.

I'm aware that high-level taxonomies can be controversial, and also that it's easy to fall into the illusion of transparency when trying to introduce a field; so suggestions for improvements are very welcome!

The key ideas are contained in this summary diagram:

First, some quick clarifications:

None of the boxes are meant to be comprehensive; we could add more items to any of them. So you should picture each list ending with “and others”.
The distinction between tasks and techniques is not a firm or standard categorisation; it’s just the best way I’ve found so far to lay things out.
The summary is explicitly from an AI-centric perspective. For example, statistical modeling and optimization are fields in their own right; but for our current purposes we can think of them as machine learning techniques.

Let’s dig into each part of the diagram now, starting from the top.

Paradigms of artificial intelligence

The field of artificial intelligence aims to develop computer programs that are able to perform useful tasks like answering questions, recognizing images, and so on. It got started around the 1950s. Historically, there have been several different approaches to AI. In the first few decades, the dominant paradigm was symbolic AI, which focused on representing problems using statements in formal languages (like logic, or programming languages), and searching for solutions by manipulating those representations according to fixed rules. For example, a symbolic AI can represent a game of chess using a set of statements about where the pieces currently are, and a set of statements about where the pieces are allowed to move (you can only move bishops diagonally, you can't move your king into check, etc). It can then play chess by searc...

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