Week 1: Foundations of machine learning
Main reference
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
Instructions: