Machine Learning

CS 5350/6350, DS 4350, Spring 2024

Loss Minimization (with a little of model selection thrown in)

This lecture generalizes an idea that we saw in the previous one: Learning can be framed as an optimization problem and can be declaratively stated as minimizing empirical risk.

The lecture ends with a discussion of model selection, the bias-variance tradeoff and cross validation.


Additional reading