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 |
![A Look Into How Data Centers Actually Work [INFOGRAPHIC]](https://www.smartdatacollective.com/wp-content/uploads/2026/08/look-data-centers-actually-work-infographic-2-330x220.jpg)
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 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)
