The stats and econ phd came from the same school, so they had more shared vocabulary, but definitely thought about problems differently. The physics colleague came from Europe and also thought about problems at a much different scale. So a lot of the initial time was spent deriving proofs so that everyone felt comfortable with different statistical methods used for analysis. The data we worked with was often small scale, sparse, and not really IID. The proofs all essentially converged, but it was interesting because different language and assumptions were made depending on their distinct disciplinary backgrounds.
Sorry for the vague speak. A lot of the work we did was confidential, so can't really talk specifics.
Statistics | Machine Learning
Dummy variable | one-hot encoding
Fitting a model | training a model
There seems to be tons of scholars who have become big names by taking results and concepts from other fields, such as heuristic/approximate dynamic optimization and game theory...
Now I would wonder - were these independent discoveries, or did they just read it & not provide citations...
I drew many, many tables on the whiteboard that day.
X causes Y if it doesn't obviously not cause Y.
This seems to conveniently overlook the decades of quantitative social science built on "I controlled for a couple things, and p is less than .05, so X causes Y."
But I'm not clear on the community context here. Is this just good-natured ribbing?
> ML: supervised learners, machines
I think ML uses the term regression for the situations where the output is numeric value (as opposed to a label), and supervised learning is more than just regression. Usually regression models have mean squared error loss function, that's one way to spot them.
An exception for this is "logistic regression" which is accepted by both the stats and ML communities.
Supervised learning is whenever you have a target variable for the observations you use to train/fit your model.
If you only have targets for some of your observations, it is called "semi-supervised learning". Although in deep learning you often talk about "pre-training" your model, which often is adjusting weights in an unsupervised way.
So you can have supervised regression as well as supervised label predictors. These can also be semi-supervised.