A lecture that reviews ideas from supervised machine learning that are relevant for understanding deep neural networks. Includes the statistical machine learning framework, principles for selecting loss functions, and the bias-variance tradeoff. The lecture ends with the surprising double-descent behavior that neural networks can perform well even when highly overparameterized.

This lecture is from Northeastern University's CS 7150 Summer 2020 class on Deep Learning, taught by Paul Hand.

The notes are available at: http://khoury.northeastern.edu/home/h...