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PKUFlyingPig/CS229
A personal dump of PDFs, slides, and notebooks from Stanford's CS229 machine learning course taught by Andrew Ng. This is someone's collected course materials, not an official resource. Useful if you want the lecture notes and slides in one place without hunting through the Stanford website.
The math foundation subdirectory (linear algebra, probability, Gaussians) is genuinely useful as standalone reference material. Covers a solid breadth of classical ML: SVM, PCA, EM, boosting, decision trees, deep learning basics. Having everything in one repo is convenient for offline study. The decision_tree_demo notebook is a decent worked example.
Last updated June 2020 — the course has moved on and these materials are stale. The README is one sentence; there's no index, no explanation of what's included or missing, no guidance on order. This is a personal archive, not a maintained educational resource — the official Stanford materials or fast.ai are better maintained alternatives. No homework solutions or answer keys, so its value as a self-study tool is limited.