[bravura]

There is a classic thereom from computational learning theory that says, if all hypotheses are equally likely, then no generalization can happen. Ie bias is necessary for learning.

To respond to some sibling comments: Yup, this is prejudice. I'll try to analogize the thereom with an example: Without prejudice, you can't recognize a leaf in a figure, because alternate hypotheses (there are an arbitrary number of things in this universe that look like leaves but in fact are not) are equally likely.

My advisor one told me that machine learning is the study of biases.

"Without the aid of prejudice and custom, I should not be able to find my way across the room." - William Hazlitt

[etrautmann]

Isn't this more a study of priors and statistics than bias? Bias would be an error between some latent underlying value and an estimate of it.