I'm not really sure who the intended audience is here. There's a lot of material covered very briefly in a very short space, and not enough details that anyone who doesn't already know it would be able to pick up anything substantive.
The minimum audience would be those who've taken an introductory point set topology, introductory analysis course, and a probability/statistics course or read extensively on the subject. If you do not have that background, you are not qualified to understand this material, no matter how much the author attempts to dumb it down
As a self-taught programmer, I appreciate learning about mathematical topics from the bottom up (from a « pure mathematics » point of view), after having gained some intuition. It is easier for me to grasp because it relates very much to my daily life of programming, with its type systems, class transformations, mappings. And the author is right in that probability theory is often employed in even looser terms than other areas of mathematics. It feels to me like building up something in java/c++/haskell vs building up the same thing in python/javascript. For a lot of people, python is simpler to handle, but I usually have to go back to my c++ to feel reasonably safe that I’m applying my functions to the right objects.
Do you know of anything which can help people get over the hurdle to know enough to use this content?
For me it's only worked when colleagues have explaining concepts to me when they were needed, after a several occurrences of this everything finally started to make sense and I could then make use of material like this.
There are no shortcuts with math. If you really want to learn it, you must be willing to put in a large number of hours over a long period time in order to master it. Are you willing to do that?
