Someone Actually Did the Homework for the MML Book

What it does A personal archive of worked solutions to the end-of-chapter exercises in Mathematics for Machine Learning, covering chapters 2 through 7. The author also shared exploratory Python notebooks—Mix of Gaussians gets an explicit callout—used to manually grind through concepts that did not stick on the first pass.

The interesting bit This is not a library or framework; it is a public study log. The value lies in seeing another human actually work through the proofs and algebra that most readers quietly skip, complete with an honest admission that some sections were genuinely tricky.

Key highlights

• Solutions for chapters 2–7 offered as both Jupyter notebooks and PDFs
• Exploratory Python code for difficult concepts, with Mix of Gaussians singled out as tricky
• PDF mirrors included because the notebook LaTeX can take time to render
• 1,251 stars suggest it has become a de facto companion for self-learners
• A Misc folder whose contents remain charmingly unspecified

Caveats

• Coverage stops at chapter 7; later chapters are absent from the README
• The Misc section is described only as Misc:), so its utility is unclear
• Notebook LaTeX rendering is slow, per the author’s warning

Verdict Useful as a companion if you are working through the MML textbook alone and want to compare your scratch work. If you need reusable machine-learning infrastructure, look elsewhere.