mml-book/mml-book.github.io

Companion webpage to the book "Mathematics For Machine Learning"

Jupyter Notebook

16,104

329 commits

updated Mar 13, 2025

See the code

README

mml-book.github.io

Companion webpage to the book "Mathematics For Machine Learning"

https://mml-book.com

Copyright 2020 by Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong. To be published by Cambridge University Press.

We are in the process of writing a book on Mathematics for Machine Learning that motivates people to learn mathematical concepts. The book is not intended to cover advanced machine learning techniques because there are already plenty of books doing this. Instead, we aim to provide the necessary mathematical skills to read those other books.

We split the book into two parts:

  • Mathematical foundations
  • Example machine learning algorithms that use the mathematical foundations

We aim to keep this book reasonably short, so we cannot cover everything. We will also provide exercises for part 1 and jupyter notebooks for part 2 of the book.

The notebooks can be run live on Binder.

Alternatively try them directly on Google Colab

TitleTutorial NotebookSolution
Linear RegressionOpen In ColabOpen In Colab
Principal Component Analysis (PCA)Open In ColabOpen In Colab
Gaussian Mixture Model (GMM)Open In Colab Open In Colab

mml-book/mml-book.github.io

Companion webpage to the book "Mathematics For Machine Learning"

Jupyter Notebook

16,104

329 commits

updated Mar 13, 2025

See the code

README

mml-book.github.io

Companion webpage to the book "Mathematics For Machine Learning"

https://mml-book.com

Copyright 2020 by Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong. To be published by Cambridge University Press.

We are in the process of writing a book on Mathematics for Machine Learning that motivates people to learn mathematical concepts. The book is not intended to cover advanced machine learning techniques because there are already plenty of books doing this. Instead, we aim to provide the necessary mathematical skills to read those other books.

We split the book into two parts:

  • Mathematical foundations
  • Example machine learning algorithms that use the mathematical foundations

We aim to keep this book reasonably short, so we cannot cover everything. We will also provide exercises for part 1 and jupyter notebooks for part 2 of the book.

The notebooks can be run live on Binder.

Alternatively try them directly on Google Colab

TitleTutorial NotebookSolution
Linear RegressionOpen In ColabOpen In Colab
Principal Component Analysis (PCA)Open In ColabOpen In Colab
Gaussian Mixture Model (GMM)Open In Colab Open In Colab

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