Math for Machine Learning: 12 Must-Read Books
It is possible to design and deploy advanced machine learning algorithms that are essentially math-free and stats-free. People working on that are typically professional mathematicians. These algorithms are not necessarily simpler. See for instance a math-free regression technique with prediction intervals, here. Or supervised classification and alternative to t-SNE, here. Interestingly, this latter math-free machine learning technique was used to gain insights about a very difficult pure math problem in number theory.
Math-free is a misnomer, in the sense that it still requires middle school arithmetic. But the author of these techniques — a real mathematician — considers that middle school arithmetic (the way it is taught) is not math, but instead, mechanical manipulations. However, for the majority of machine learning professionals, a good math and statistical background is required. Everyone agrees on that these days, a change compared to 10 years ago. The following books serve that purpose.
Books Focusing on the Math
The following books were published in the last 2–3 years. They rapidly gained a lot of popularity. These books were written with modern machine learning applications in mind. Usually free, they are available online or in PDF format, and have their own websites. Some have a print version, which is useful for annotations or bed reading.
Read the full article featuring 12 math books, with 4 books focusing on coding aspects, here
