Week 1: Foundations of machine learning

Main reference

ISLR 2.1, 2.2

What you will learn this week

  • Framing the problems
  • Notation and math
  • Bias variance-tradeoff
  • Fitting your models: training/test splits, optimisation
  • Measuring fit: accuracy, loss
  • Diagnostics: residuals
  • Feature engineering: combining variables to better match purpose and help the model fitting

Lecture slides

Tutorial instructions