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It is concerning to me how many people are deploying these techniques without first understanding the principles and limitations of multivariate regression and

by hodder 8y ago

It is concerning to me how many people are deploying these techniques without first understanding the principles and limitations of multivariate regression and time series analysis. I'm not sure why people are diving into modern techniques without knowing how to properly specify a simple (but powerful) regression model.

liotier 8y ago
> I'm not sure why people are diving into modern techniques without knowing how to properly specify a simple (but powerful) regression model. Because math is hard whereas the ML hype carries the promise of effortless automagic - overkill, and unclear fitness be damned !
mindcrime 8y ago
One might reply by saying: It is concerning to me that people are driving cars without first understanding the principles of mechanical engineering, thermodynamics, and fluid dynamics. I'm not sure why people are diving into modern cars without knowing how to properly build a simple (but powerful) steam engine. The point being, there's a distinction between "machine learning research" and "applied machine learning". Of course those points are on a continuum, not separated by a bright line. But the point is, there are different roles and those different roles have different goals and requirements. Of course knowing more math and theoretical foundations enables you to do more in some senses... just like a basic knowledge of fluid dynamics would be useful if you want to "port and polish" the cylinder heads in your car. But in reality, a minuscule portion of the population of car owners will ever want to do this. OTOH, it is essential if you're the person designing the car engine in the first place. No, it's not a perfect analogy, but I think the overall point stands: some people need to know the deep, deep details of linear algebra, multivariable calculus, probability theory, measure theory, topology, etc., etc., for their goals in machine learning, while other people can achieve quite a lot without all of that knowledge.

by hodder 8y ago

liotier 8y ago

> I'm not sure why people are diving into modern techniques without knowing how to properly specify a simple (but powerful) regression model. Because math is hard whereas the ML hype carries the promise of effortless automagic - overkill, and unclear fitness be damned !

mindcrime 8y ago

One might reply by saying: It is concerning to me that people are driving cars without first understanding the principles of mechanical engineering, thermodynamics, and fluid dynamics. I'm not sure why people are diving into modern cars without knowing how to properly build a simple (but powerful) steam engine. The point being, there's a distinction between "machine learning research" and "applied machine learning". Of course those points are on a continuum, not separated by a bright line. But the point is, there are different roles and those different roles have different goals and requirements. Of course knowing more math and theoretical foundations enables you to do more in some senses... just like a basic knowledge of fluid dynamics would be useful if you want to "port and polish" the cylinder heads in your car. But in reality, a minuscule portion of the population of car owners will ever want to do this. OTOH, it is essential if you're the person designing the car engine in the first place. No, it's not a perfect analogy, but I think the overall point stands: some people need to know the deep, deep details of linear algebra, multivariable calculus, probability theory, measure theory, topology, etc., etc., for their goals in machine learning, while other people can achieve quite a lot without all of that knowledge.