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Notes for CMSC 828W: Foundations of Deep Learning (Fall 2020) taught by Soheil Feizi
My notes are intended to be a concise reference for myself, not a comprehensive replacement for lecture.
Basics
A refresher of Machine Learning and Supervised Learning.
Empirical risk minimization (ERM)
Minimize loss function over your data:
\(\displaystyle \min_{W} \frac{1}{N} \sum_{i=1}^{N} l(f_{W}(x_i), y_i))\)
Loss functions
For regression, can use quadratic loss:
\(\displaystyle l(f_W(x), y) = \frac{1}{2}\Vert f_W(x)-y \Vert^2\)
For classification, can use hinge-loss:
\(\displaystyle l(f_W(x), y) = \max(0, 1-yf_W(x))\)
Misc
Visible to::users
Resources
