Machine Learning

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.

Lectures

• Learning as Loss Minimization

• Cross-Validation

• Bias-Variance Tradeoff

• Videos:
  • Learning as loss minimization
• Older videos:
  • Learning as loss minimization

Links

• Chapter 7 from The Elements of Statistical Learning, by Hastie, Tibshirani and Friedman. (Available online)

Additional reading

• Pedro Domingos, A Unified Bias-Variance Decomposition