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My question: I'm looking for a taxonomy/bestiary/overview of machine learning techniques. I would like to learn 1) how these methods relate to each other, and 2) the relative costs and benefits (and perhaps typical applications) of the different approaches.
Background: I'm trained in statistics, and have a reasonably clear mental map of how a range of these techniques relate to each other. Understanding the costs and benefits of different techniques obviously makes it easier to select the best one to apply in different situations.
I would like to develop a similar mental map or taxonomy of the broader machine learning field. It's most important for me to understand the highest levels in the technique taxonomy, but I also recognise that there are major lower-level developments in some areas I should be aware of (e.g. neural networks seem to have a huge number of sub-classes).
I'm not looking for in-depth explanations of each method - though references would be great - but instead a framework I can use to focus my learning efforts in a more informed way.
This question focussed on statistical techniques is similar, in that the goal is to understand relationships between methods. But I'm looking for more than a 'cheat sheet'. I'd like to understand each at least at a basic level, and not just follow a set of rules on a flow chart.