Bias and variance of an estimator: the case of the MLE

We discuss the question of the quality of an estimator. Given different training datasets, how close is an estimator to the real value of a parameter (what is its bias) and how spread are those estimations (what is its variance). We show that the mean square error of an estimator is the square of its bias plus its variance.
This video is part of a full course on Foundations of Machine Learning that is freely available at • Welcome to the Foundat... .
Coding assignments: github.com/ion...

[@sriramgugulothu4195]

You saved my day sir!!!!!1

[@MLClassroom]

Welcome!

[@xondiego]

The notation is a bit odd but still the concepts are explained in a great extent. Thanks.

[@xondiego]

Hi there, do you have some videos about likelihood ratio, and/or UMP test? Thanks again for all the content posted!

[@MLClassroom]

No such content yet.

[@xondiego]

@@MLClassroom thanks for text me back. Again great work in here.

[@MLClassroom]

@@xondiego thank you for the positive feedback, greatly appreciated!

[@siddharthdedhia11]

I have some confusion regarding this, could you provide an example with numbers? It would be much clearer then.

[@MLClassroom]

I do not have one ready right away.