[rahimiali]

a good machine learning course might indeed cover 1940-1980s operations research (nonlinear optimization, linear/quadratic programming, dynamic programming), and statistics from 1970-1990s (graphical models, markov chain monte carlo methods, measures of model capacity). i'd say the field borrows the most useful bits from these fields and finds good honest use in many real life problems today. and i agree that there's a lot of unwarranted hype that leads to a lot of well-deserved skepticism.