Companion webpage to the book “Mathematics for Machine Learning”. Copyright 2019 by Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong. To be published by Cambridge University Press.
Please link to this site usinghttps://mml-book.com.
Twitter:@ mpd 37,@ AnalogAldo,@ChengSoonOng.
We wrote 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.
The book will bepublished by Cambridge University Pressin early
We split the book into two parts:
- Mathematical foundations
- Example machine learning algorithms that use the mathematical foundations
We aim to keep this book fairly short, so we don’t cover everything.
We will keep PDFs of this book freely available after publication.
Download thePDF of the book
Table of Contents
Part I: Mathematical Foundations
- Introduction and Motivation
- Linear Algebra
- Analytic Geometry
- Matrix Decompositions
- Vector Calculus
- Probability and Distribution
- Continuous Optimization
Part II: Central Machine Learning Problems
- When Models Meet Data
- Linear Regression
- Dimensionality Reduction with Principal Component Analysis
- Density Estimation with Gaussian Mixture Models
- Classification with Support Vector Machines
Report errata and feedback.
We submitted the final draft for copy-editing. Therefore, any issues you raise now may not make it into the printed version.
Tutorials
We are working on jupyter notebook tutorials for the machine learning parts:
- Linear Regression
- Gaussian Mixture Models
- PCA
- SVM (work in progress)
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