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I've read that machine learning is making statistics classes fall out of favor. I can understand why. In a lot of ways, machine learning is statistics in reve

I've read that machine learning is making statistics classes fall out of favor. I can understand why. In a lot of ways, machine learning is statistics in reverse. Instead of starting with a hypothesis and trying to reject the null hypothesis, unsupervised machine learning starts with data and generates hypothesis.

arcanus 11y ago
It's more than just statistics, broadly speaking that is the scientific process.
- vonmoltke 11y ago
  It is part of the scientific process, just as it is part of the machine learning process. The hypothesis has to come from somewhere. In classic science, it comes from a scientist or group of scientists who notice something strange or see a correlation, devise an explanation, then test it to see of it is correct. Unsupervised machine learning just automates the second and part of the first step by having one or more algorithms devise the hypotheses instead of humans. ML is still supposed to do the third step. IMO, where ML often falls down due to the immaturity of the field is in not creating good experiments to test the models (hypotheses) generated by the algorithms.
  - pwm 11y ago
    Side note: Zen and the Art of Motorcycle Maintenance is a classic read about "The hypothesis has to come from somewhere".
beejiu 11y ago
I guess there's a difference in perspective if you take your machine learning classes with the Mathematics/Stats department rather than the Computer Science department.
gbrown 11y ago
I think Statistics departments have lagged behind in teaching ML techniques as first class citizens of the statistical world, but also that ML folks tend to gloss over or ignore a lot of the benefits formal inference and statistical thinking bring to the table. Traditional statistics is very very good at helping us learn a lot about relatively simple (or carefully and deliberately simplified) processes, and provides a rich background in study design. ML techniques are good at helping us learn a little bit about arbitrarily complicated processes, and apply that knowledge quickly. A modern practitioner in either field should have a working knowledge of both [families of] paradigms.
Cacti 11y ago
I'm not even sure it's "in reverse." The supervised portion of ML is almost entirely regular ol statistics.

arcanus 11y ago

It's more than just statistics, broadly speaking that is the scientific process.

vonmoltke 11y ago
It is part of the scientific process, just as it is part of the machine learning process. The hypothesis has to come from somewhere. In classic science, it comes from a scientist or group of scientists who notice something strange or see a correlation, devise an explanation, then test it to see of it is correct. Unsupervised machine learning just automates the second and part of the first step by having one or more algorithms devise the hypotheses instead of humans. ML is still supposed to do the third step. IMO, where ML often falls down due to the immaturity of the field is in not creating good experiments to test the models (hypotheses) generated by the algorithms.
- pwm 11y ago
  Side note: Zen and the Art of Motorcycle Maintenance is a classic read about "The hypothesis has to come from somewhere".

beejiu 11y ago

I guess there's a difference in perspective if you take your machine learning classes with the Mathematics/Stats department rather than the Computer Science department.

gbrown 11y ago

I think Statistics departments have lagged behind in teaching ML techniques as first class citizens of the statistical world, but also that ML folks tend to gloss over or ignore a lot of the benefits formal inference and statistical thinking bring to the table. Traditional statistics is very very good at helping us learn a lot about relatively simple (or carefully and deliberately simplified) processes, and provides a rich background in study design. ML techniques are good at helping us learn a little bit about arbitrarily complicated processes, and apply that knowledge quickly. A modern practitioner in either field should have a working knowledge of both [families of] paradigms.

Cacti 11y ago

I'm not even sure it's "in reverse." The supervised portion of ML is almost entirely regular ol statistics.