ilmoi/MML-Book

Code / solutions for Mathematics for Machine Learning (MML Book)

What it solves

This project provides a public record of self-study solutions and experimental code for the "Mathematics for Machine Learning" textbook. It helps learners verify their own work and see how to implement complex mathematical concepts in Python.

How it works

The repository is structured into two main parts: Jupyter Notebooks containing step-by-step solutions to end-of-chapter exercises, and Python scripts used to explore and visualize specific mathematical concepts, such as Gaussian mixtures.

Who it’s for

It is designed for students, researchers, or self-learners who are actively working through the "Mathematics for Machine Learning" book and want to compare their solutions or see practical code implementations of the theory.

Highlights

  • Comprehensive solutions for chapters 2 through 7.
  • Available in both interactive Jupyter Notebook and static PDF formats.
  • Includes experimental Python code for hands-on concept exploration.
  • Serves as a reference for understanding challenging topics like Gaussian mixtures.

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