AlbertSchool — Mathematics Foundations

Mathematics Foundations — AlbertSchool

Four 3-hour sessions covering the linear algebra and calculus behind machine learning: vectors and cosine similarity, matrices as transformations and the forward pass, derivatives and the chain rule as backpropagation, and gradient descent.

Slides are deliberately mostly blank — most of the content is written live on the iPad during class — so the PDFs below are closer to a skeleton than finished lecture notes.

LaTeX sources live in a private repository; the distributed PDFs are published to github.com/cetiennec/cours-pdf and served straight from GitHub — click a title below to preview it inline, or “open” for the full-screen PDF.

Sessions

1 — Vectors, Geometry & Similarity open ↗

2 — Matrices, Transformations & Eigenstructure open ↗

3 — Derivatives and the Chain Rule open ↗

4 — Gradients, Gradient Descent & Assessment open ↗

Session 1: vector addition and the dot product, cosine similarity on real data. Session 2: matrices as linear transformations, the forward pass, eigenvectors and PCA. Session 3: derivatives, differentiation rules, the chain rule as backpropagation. Session 4: the gradient, gradient descent, minima, and an applied check.