Ways to think about machine learning
It&#x27;s tough to know just what <i>machine learning</i> covers, includes, consists of.  From what I&#x27;ve been able to see, currently in practice, apparently 90+% of machine learning is <i>curve fitting</i> and nearly all of that is some form of classic linear regression.<p>Linear regression and curve fitting more generally have been around, available, and used going way back in electronic digital computing and well before.  E.g., for software we&#x27;ve long had the IBM Scientific Subroutine Package (SSP), SPSS (Statistical Package for the Social Sciences), SAS (Statistical Analysis System), ..., R.  There are stacks of polished textbooks in statistics and specialized to some fields, e.g., econometrics, time series analysis, etc.   There has been some usage, but headlines have been rare for decades.<p>But, there&#x27;s a lot more to applied math than anything much like that curve fitting.  E.g., maybe take the <i>learning</i> as the ability to do statistical estimates -- well, there&#x27;s a lot to statistical estimation than curve fitting.<p>There&#x27;s also the field of optimization -- linear programming, integer linear programming, network linear programming, dynamic programming (discrete time, continuous time, deterministic, under uncertainty), quadratic programming, other cases of non-linear programming, and more.<p>And lot more can be done in applied probability and stochastic processes.<p>IMHO it would be better progress and description of the current state to discuss  both the applications and the solution techniques in more detail.<p>E.g., the crucial core of my startup is some applied math I derived (with theorems and proofs) based mostly on some advanced pure math prerequisites.  I wrote the corresponding software.  Then from 50,000 feet up or from the point of view of a user of the work, what I did can look like a <i>machine</i> that <i>learns</i> a lot very quickly, is very <i>smart</i>, and puts out really <i>intelligent</i> stuff.  Still, my math is not covered by any of the machine learning I&#x27;ve heard of.<p>Point:  There&#x27;s a LOT of applied math that can be done and, really, has been done far from current descriptions of machine learning.