Re: Learning Approach vs. Function Approximation
Thank you, Sir. Your response helps in drawing the distinctions between developments in statistics & ML. As I read further, I think I need to keep an eye on: (a) core objective, (b) the formulation of the problem, and (c) the assumptions made. As you note, and I vaguely sensed that too, similarities between stats & ML approaches appear on the subject of regression, and then too, one needs to pay attention to assumptions made during problem formulation (eg. distributional assumptions on input data etc.). Your point on weaker assumptions in ML, generally speaking, is very helpful too. And finally, if one examines the various ML paradigms, for eg., as stated in Sec 1.2, the objectives may be dramatically different, and we get problems & formulations quite different from statistics.
