I don't think a piece on information theory should necessarily be "accessible to many people". It's a topic which is normally taught in grad school.
Something like X ~ p(x) would be seen all over the place in probability, stats, ML, and related courses such as info theory, detection and estimation, etc. Likely by the time someone is interested in info theory this notation would be permanently etched into their minds. So for this article it is very "audience appropriate".
> not a criticism and I don't have a good solution
Having a mental map of how different subjects fit together (without actually having to studying them in-depth) is a good start.
I've seen so many people crash and burn with machine learning because they were unaware that it depends on linear algebra, calculus, and probability.
With a mental map there is less "surprise" and it's more a matter of simply understanding that they didn't have the right dependencies.
Something like X ~ p(x) would be seen all over the place in probability, stats, ML, and related courses such as info theory, detection and estimation, etc. Likely by the time someone is interested in info theory this notation would be permanently etched into their minds. So for this article it is very "audience appropriate".
> not a criticism and I don't have a good solution
Having a mental map of how different subjects fit together (without actually having to studying them in-depth) is a good start.
I've seen so many people crash and burn with machine learning because they were unaware that it depends on linear algebra, calculus, and probability.
With a mental map there is less "surprise" and it's more a matter of simply understanding that they didn't have the right dependencies.