[graycat]

Gee, guys, looking at the list of topics, a huge fraction, likely over 50%, of the material goes back to programs in operations research, statistics, and the mathematical sciences from about 1970 on. Nearly the only thing new is the collection of sample applications. From what I've seen, the quality of the content of the current ML courses is way below that going back to 1970.

Warning: History shows that the US economy looked at the material in operations research, statistics, and the mathematical sciences and rolled their eyes, did a big upchuck, laughed, turned, and walked away. One might look for alarms from their hype and fad detectors.

[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.