Mathematics for Machine Learning Project Details

Project Overview

Mathematics for Machine Learning is a textbook co-authored by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong, published by Cambridge University Press in 2020. This project aims to provide machine learning learners with the necessary mathematical foundations, rather than focusing on advanced machine learning techniques.

Project Goals

The book's goal is to motivate the learning of mathematical concepts, rather than covering advanced machine learning techniques, as there are already numerous books doing so. Instead, the authors aim to provide the necessary mathematical skills required to read other books.

Core Features

1. Free Access

The authors promise "We will keep the PDF version of this book freely available."

2. Practical Orientation

The essential mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability, and statistics.

3. Conciseness

The authors state, "Our goal is to keep this book reasonably short, so we will not cover everything."

Book Structure

The book is divided into two main parts:

Part I: Mathematical Foundations

• Linear Algebra
• Analytic Geometry
• Matrix Decompositions
• Vector Calculus
• Optimization
• Probability and Statistics

Part II: Central Machine Learning Problems

The book uses these concepts to derive four core machine learning methods: linear regression, principal component analysis, Gaussian mixture models, and support vector machines.

Target Audience

For students and others with a mathematical background, these derivations provide a starting point for machine learning textbooks.

Academic Reviews

The book has received high praise from the academic community:

Joelle Pineau, McGill University & Facebook:

"This book provides an excellent coverage of all the essential mathematical concepts for machine learning. I look forward to sharing it with students, colleagues, and anyone interested in building a solid understanding of the fundamentals."

Christopher Bishop, Microsoft Research Cambridge:

"The field of machine learning has developed rapidly in recent years, and the range of successful applications is becoming ever more impressive. This comprehensive textbook covers the key mathematical concepts that underpin modern machine learning, with a strong focus on linear algebra, calculus, and probability."

Pieter Abbeel, University of California, Berkeley:

"This book provides a beautiful exposition of the mathematics underpinning modern machine learning. Highly recommended to anyone seeking a one-stop shop for a deep understanding of the foundations of machine learning."

Project Resources

Official Website

• Main Website: https://mml-book.com
• GitHub Page: https://mml-book.github.io/

Author Information

• Marc Peter Deisenroth - Twitter: @mpd37
• A. Aldo Faisal - Twitter: @AnalogAldo
• Cheng Soon Ong - Twitter: @ChengSoonOng

Access

• Free PDF version available for download from the official website
• Printed version published by Cambridge University Press
• Supporting materials and errata are continuously updated on the official website

Supplementary Resources

Others have created resources to support the book's material, including:

• Exercise solutions
• Jupyter notebooks
• Related learning materials and further reading

Project Significance

This project fills an important gap in machine learning education, where these topics are traditionally taught in separate courses, making it difficult for data science or machine learning students to build a unified mathematical foundation. By providing a comprehensive mathematical framework, the project provides learners with a solid foundation needed to enter the field of machine learning.