Math skills to survive other machine learning books

What it does Hosts the companion site for Mathematics for Machine Learning, a textbook by Deisenroth, Faisal, and Ong. It supplies exercises for the book’s mathematical foundations and runnable Jupyter notebooks covering linear regression, PCA, and Gaussian mixture models. Everything runs in-browser via Binder or Google Colab.

The interesting bit The authors explicitly avoid advanced machine learning techniques; their goal is to give you the minimum mathematical literacy needed to read the rest of the ML literature without drowning. In a field crowded with doorstop textbooks, the promise of brevity is almost rebellious.

Key highlights

• Split into mathematical foundations and example ML algorithms
• Three runnable tutorials: Linear Regression, PCA, and Gaussian Mixture Models
• Each tutorial ships with a companion solution notebook
• Executes in-browser via Binder or Google Colab
• Scope is explicitly limited; the authors aim to keep the book “reasonably short”

Caveats

• The README states the book is still being written (“in the process of writing”) despite a 2020 copyright line
• Only three tutorial notebooks are listed, so the hands-on portion is tightly focused rather than comprehensive

Verdict Worth bookmarking if you need to rebuild math fundamentals before tackling ML research, or if you teach introductory courses. Pass if you are looking for advanced machine learning techniques—the authors explicitly left those to other books.