Foundations
Note
This section is in progress. The Machine Learning overview and the Helsinki Python track are already here; new primer pages will appear as they are written.
Why Foundations
This is where the path starts: what machine learning is, how a model learns, and the Python I use to think on the page.
Python lives here on purpose. It is not a side menu. The Helsinki Python Programming MOOC 2024 notes are part of the foundation of this wiki, and they stay.
Already here
- What is Machine Learning — the decision / error / optimisation loop, and the four learning paradigms (supervised, unsupervised, semi-supervised, reinforcement).
- Python Programming — my notes from the University of Helsinki MOOC (
programming-24.mooc.fi). Parts 1–3 are real notes; part 4 is started (parameter vs argument); parts 5–14 are still stubs I will fill.
Coming next
- Short primers on linear algebra and statistics, when they earn their place next to a real page.
- A later “Python for ML” track (NumPy, pandas, matplotlib, notebooks) beside the Helsinki notes, not instead of them.