01
Supervised learning and generalisation
Why is fitting the training data the easy part?
ReadingFree
The Elements of Statistical Learning ↗Hastie, Tibshirani & Friedman
Free PDF from the authors. The standard reference.
AI · Module 12
Machine Learning Foundations
Before the deep learning hype: what learning from data actually is, statistically. Skipping this is why so many practitioners cannot debug a model.
By the end of this module
You can explain what a model is fitting, what it is assuming, and how it will fail.
0%0 / 4 complete
02
Bias, variance and regularisation
Why does a more powerful model sometimes do worse?
ReadingFree
An Introduction to Statistical Learning ↗James, Witten, Hastie & Tibshirani
Free PDF, with labs in R and Python.
03
Linear models, trees, SVMs, ensembles
When does the boring model beat the sophisticated one?
04
Clustering and dimensionality reduction
What can you learn with no labels at all?
Quiz
Not written yet for this module. The lectures above are complete and the module still counts toward your progress.