• 01 Distribution Visualizer PDF/CDF visualizer for Normal, Uniform, Exponential, Laplace, Dirac, and Mixture distributions. Statistics
• 02 Unsupervised Supervised Learning Step through a demo to show how to solve a supervised classification problem using unsupervised density estimation. Machine learning
• 03 Linear Regression Regularization Interactive comparison of several linear regression regularization methods and their results. Machine learning
• 04 Estimation: Bias, Variance, MSE, MLE Notes on point/function estimation, standard error, minimizing MSE, consistency, and likelihood objectives. Statistics
• 05 Bayesian Inference Priors, posteriors, conjugacy, MAP, posterior predictive, and approximate inference (MCMC/VI). Statistics
• 06 Principal Component Analysis Interactive walkthrough of PCA via SVD, principal directions, covariance eigendecomposition, and reconstruction. Machine learning
• 07 Fourier Image Decomposition DFT periodicity assumption, low-pass filtering, the Gibbs phenomenon, and why non-periodic images produce checkerboard artifacts. Signals
• 08 Manifold Learning An animated Isomap walkthrough plus a step-by-step side-by-side comparison of manifold learning algorithms on synthetic datasets or your own CSV. Machine learning