mml-book/mml-book.github.io

This repository is the companion webpage and resource hub for the book "Mathematics For Machine Learning," written by Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong, to be published by Cambridge University Press. The book is specifically aimed at people who want to understand machine learning at a deeper level but need to build up their mathematical foundations first. Rather than covering advanced machine learning techniques, which many other books already do, this one focuses on giving readers the mathematical tools required to read those other books. The book is split into two parts: the first covers mathematical foundations, and the second shows example machine learning algorithms that put those foundations into practice. Exercises accompany part one, and Jupyter notebooks (interactive coding documents that combine explanations and runnable code) accompany part two. The repository hosts tutorial notebooks for several algorithms including Linear Regression, Principal Component Analysis (PCA, a technique for reducing the number of variables in a dataset while keeping the most important patterns), and Gaussian Mixture Model (GMM, a statistical model that assumes data comes from a mixture of several distributions). These notebooks can be run live in a browser through Google Colab or Binder without installing any software locally. The full content of the book and tutorials is available at mml-book.com.