‘The field of machine learning has grown dramatically in recent years, with an increasingly impressive spectrum of successful applications. This comprehensive text covers the key mathematical concepts that underpin modern machine learning, with a focus on linear algebra, calculus, and probability theory.

A book that motivates people to learn mathematical concepts for machine learning, covering linear algebra, calculus, probability, and optimization.

2020年4月23日 · Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, probability and statistics that are used in machine learning.

Please link to this site using https://mml-book.com. Other people have created resources that support the material in this book: Machine Learning Tokyo is having an online reading group; Jupyter notebooks for Chapter 2-7; implementation and visualization of examples by Vince Bartle; Jupyter notebook for Chapter 9 by @zotroneneis

2023年10月8日 · It covers the fundamental mathematical tools needed to understand machine learning, including linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability, and statistics.

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 …