Please link to this site using https://mml-book.com.
Twitter:@mpd37, @AnalogAldo, @ChengSoonOng.
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 fairly short (tried for 300 pages, now close to 400 pages), so we don't cover everything.
We will keep PDFs of this book freely available after publication.
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
{:start="8"} 8. When Models Meet Data 9. Linear Regression 10. Dimensionality Reduction with Principal Component Analysis 11. Density Estimation with Gaussian Mixture Models 12. Classification with Support Vector Machines