3 articles
Complete CS229 lecture notes for the EM algorithm and Gaussian Mixture Models. Step-by-step derivation from Jensen's inequality to the E-step responsibilities and M-step closed-form updates, plus how EM relates to k-means and PCA.
Costin Gheorghe
Every topic in Stanford CS229, organized by lecture in one readable page: linear regression, GLMs, SVMs and kernels, neural networks, learning theory, k-means, EM, and reinforcement learning. A study reference to work through alongside the course, not a replacement for Andrew Ng's official PDFs.
Costin Gheorghe
Complete CS229 lecture notes for the SVM dual formulation. Step-by-step derivation from the primal margin problem to the dual using Lagrange multipliers and KKT conditions, plus why the dual form is what makes the kernel trick possible.
Costin Gheorghe