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// ◇ Classical Machine Learning

Principal Component Analysis (PCA)

Reduces dimensions by keeping directions of greatest variance.

主成分分析 1901 Beginner core

Original paper ↓ What to learn first →

IN PLAIN WORDS

Rotates data onto axes ordered by variance; keeping the top few compresses, denoises and visualizes. The low-rank intuition behind LoRA is related.

Used in: Visualization, feature compression, embedding reduction

TIMELINE · prerequisites → this → what builds on it

1901 Principal Compone…2008 t-SNE2018 UMAP2021 LoRA (Low-Ran…2022 Superposition

01Original paper

LIII. On lines and planes of closest fit to systems of points in space

Pearson et al. · The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science · 1901

DOI ↗ ✓ Verified · Crossref API doi:10.1080/14786440109462720

02Where it sits

Learn the left column first; the right column builds on this one.

Learn first

Principal Component Analysis (PCA)

Then you can learn

Linear Regressi…t-SNEUMAPLoRA (Low-Rank …Superposition Principal Component…

Left: prerequisites · right: builds on this · dashed: related. Full map →

03Learning resources

Official docs, explainers, courses and reference code. All links checked.

Official docs

Books

04Where the course teaches it

U11 Linear Algebra Vector spaces, matrix multiplication, eigenvalues and SVD — the language of embeddings, attention and LoRA. 15 h2 algorithms0 papers U23 Unsupervised Learning k-means, Gaussian mixtures and EM, PCA, t-SNE/UMAP. 8 h6 algorithms2 papers

On learning paths:Zero to Deep Learning

For AI / data

Data updated: AI algorithms & curriculum(UTC+8)