Machine Learning

For roughly a decade, XGBoost was the single algorithm most likely to win a Kaggle competition that did not involve images or natural language. It is still, on tabular data, the...

Every model in machine learning, every system identification routine, every adaptive controller has an optimization problem at its core. This reference walks through the algorit...

An interactive reference on regression — predicting continuous numbers from data. Nine algorithm families compared, four live demos, and an honest treatment of the data and metr...

An illustrated essay on support vector machines and their one-class variant — the maximum-margin hyperplane, soft margins, the kernel trick, and the surprising move that turns a...

A field guide to the six paradigms of machine learning — supervised, unsupervised, reinforcement, imitation, transfer, and the rapidly-merging boundaries between them. The mecha...

A 22-chapter field guide to modern AI systems — tokenization, attention, LLMs, prompting techniques, RAG, agent frameworks, MCP, and the engineering considerations around them.

A working Bayesian optimization loop you can run in your browser. Watch Expected Improvement, UCB, and PI compete to find the minimum of a deceptive benchmark function — and rea...

Click, slide, and watch the posterior update. A working intuition for Gaussian Processes — from the one-sentence definition through the Cholesky math and the honest O(n³) scalin...

An illustrated primer on survival analysis. Why time-to-event data is fundamentally different from regression. Censoring, hazard functions, Cox's elegant trick, and the neural e...

An illustrated primer on the Isolation Forest — the anomaly-detection algorithm that flips the usual approach on its head. Five interactive figures showing isolation paths, the ...

An illustrated primer on autoencoders and variational autoencoders. The bottleneck principle, what lives in the latent space, the variational reformulation that turned an unsupe...