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:

Machine learning	Statistics
Glossary
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:

Machine learning	Statistics
Glossary
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

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…