The difference between Statistics and Machine Learning

I get my daily R fortune by following Rfortunes on Twitter. This one came up the other day:

To paraphrase provocatively, ‘machine learning is statistics minus any checking of models and assumptions’. Brian D. Ripley.

In a similar vein, back in December Brendan O’Connor remarked upon Rob Tibshirani’s comparison of machine learning and statistics, reproduced here:

Glossary

Machine learning Statistics

network, graphs model

weights parameters

learning fitting

generalization test set performance

supervised learning regression/classification

unsupervised learning density estimation, clustering

large grant = $1,000,000 large grant = $50,000

nice place to have a meeting:
Snowbird, Utah, French Alps

nice place to have a meeting:
Las Vegas in August

It’s certainly a pithy comparison. Brendan O’Connor concurs that the differences between the two are more superficial than substantive, and his thoughts on the cultural differences between the two disciplines are very interesting. Amongst other things, his comparison of two similar courses in Stanford (one from the Computer Science department, one from Statistics) leads him to conclude:

ML sounds like it’s young, vibrant, interesting to learn, and growing; Stats



I get my daily R fortune by following Rfortunes on Twitter. This one came up the other day:

To paraphrase provocatively, ‘machine learning is statistics minus any checking of models and assumptions’. Brian D. Ripley.

In a similar vein, back in December Brendan O’Connor remarked upon Rob Tibshirani’s comparison of machine learning and statistics, reproduced here:

Glossary

Machine learning Statistics

network, graphs model

weights parameters

learning fitting

generalization test set performance

supervised learning regression/classification

unsupervised learning density estimation, clustering

large grant = $1,000,000 large grant = $50,000

nice place to have a meeting:
Snowbird, Utah, French Alps

nice place to have a meeting:
Las Vegas in August

It’s certainly a pithy comparison. Brendan O’Connor concurs that the differences between the two are more superficial than substantive, and his thoughts on the cultural differences between the two disciplines are very interesting. Amongst other things, his comparison of two similar courses in Stanford (one from the Computer Science department, one from Statistics) leads him to conclude:

ML sounds like it’s young, vibrant, interesting to learn, and growing; Stats does not.

So, do statisticians “merely” have an image problem in this field, or is there something more substantive at play? Perhaps protests like this are in our future…

CMU Machine learning protest

CMU machine learning students “protest” at the G20 summit in Pittsburg, September 25 2009. Photo by Arthur Gretton on Flickr.

AI and Social Science: Statistics vs. Machine Learning, fight! (via @Cmastication)

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