To elaborate on the point: When doing probabilistic modeling, whether one realizes or not, there typically is an underlying Bayesian formulation which explains what one is doing. Now, that might be well-aligned with the problem of interest (or not), and being clear on the fundamentals helps understand that, and also to compose distinct ideas which make sense together in the context of the problem. eg: see my comments below, in the context of linear regression from a Bayesian perspective.
Also, while "scaling" with data is a very hip thing these days, for most problems of interest it is very difficult/expensive to get lots of data (or afford compute). Further, humans often have very useful domain-models which are worth encoding into the structure of the model. This also helps nicely mix together a conventional "software" modeling with probabilistic aspects (for those who weren't aware, this flows towards what is called "probabilistic programming", and recent developments have made significant progress towards methods which work for an "intermediate" dimensionality, if not "large" dimensionality).
--
@astrophysician: Feels nice to see expressed so clearly, a perspective that I share! Feel free to get in touch if you'd like to discuss ML.
Additionally, you should probably learn a (little) R (which you can get from this book). This is not because R is a wonderful language (though I'm pretty fond of it myself) but because it's a great tool for the communication and expression of statistical methods.
Most good stats (which will help you be actually good at ML) books tend to either be written in mathematics, or R (or both). Given that you're already a programmer, R will probably make it easier for you to learn a bunch of this stuff (and the docs for R functions tend to point towards useful literature).
I actually travelled the other way (i.e. from stats to code) and I found R very very helpful. Of course, your mileage may vary, but the link above is probably the best single book that you could read to start learning ML.
Thank you! Providence wills that I have R studio installed to make a wordcloud, but I installed it without actually knowing what it is. (Just followed a tutorial to get my wordcloud :)
So thanks for the recommendation, looking forward reading it!
Also, while "scaling" with data is a very hip thing these days, for most problems of interest it is very difficult/expensive to get lots of data (or afford compute). Further, humans often have very useful domain-models which are worth encoding into the structure of the model. This also helps nicely mix together a conventional "software" modeling with probabilistic aspects (for those who weren't aware, this flows towards what is called "probabilistic programming", and recent developments have made significant progress towards methods which work for an "intermediate" dimensionality, if not "large" dimensionality).
--
@astrophysician: Feels nice to see expressed so clearly, a perspective that I share! Feel free to get in touch if you'd like to discuss ML.