Lihang

Why we included this project

Li Hang's Statistical Learning Methods is a staple for anyone learning classic machine learning, and this companion repo follows it chapter by chapter with notes, Jupyter notebooks, and runnable Python code for the algorithms in the book, from perceptrons and SVMs to HMMs, CRFs, and the newer unsupervised topics. It also collects supporting material that makes self-study easier: symbol and glossary indexes, an errata list, and links to the references the book cites. The maintainer adds commentary on how chapters connect, which helps when a topic like maximum entropy or MCMC feels isolated. This isn't a library you'd import into production; treat it as a study aid to work through alongside the textbook. If you're prepping for ML interviews or graduate coursework, having all these worked examples in one place saves real time.