A Blueprint for Trustworthy Machine Learning

Empirical Risk Minimization

Your browser needs to be JavaScript capable to view this video

Try reloading this page, or reviewing your browser settings

This video segment explains empirical risk minimization as a main paradigm for the design of ML methods.

Keywords

  • Empirical Risk
  • Training
  • Optimization
  • Overfitting
  • Law of Large Numbers
  • Random Variables
  • i.i.d.

About this video

Author(s)
Alexander Jung
First online
25 December 2022
DOI
https://doi.org/10.1007/978-981-19-9711-2_6
Online ISBN
978-981-19-9711-2
Publisher
Springer, Singapore
Copyright information
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023