A Beginner Mathematics Book For Machine Learning.

Oct. 19, 2018, 4:40 p.m. By: Kirti Bakshi


Today, there are a number of books that aim at familiarizing its audience with advanced techniques of machine learning.

But, a book in process: “Mathematics for Machine Learning”, will uncover itself keeping the beginners in the area of machine learning in mind.

The book, that is meant to be for beginners mainly aims at motivating people to learn mathematical concepts and therefore does not intend to cover any advanced machine learning techniques since there already are a number of books doing the same.

The aim here is to provide the readers with all the necessary mathematical skills so that they can efficiently read those other books.

What all areas will the book cover?

The book is to be split into two parts:

Mathematical Foundations:

  • Introduction and Motivation

  • Linear Algebra

  • Analytic Geometry

  • Matrix Decompositions

  • Vector Calculus

  • Probability and Distribution

  • Continuous Optimization

Example Machine Learning Algorithms That Use The Mathematical Foundations:

  • When Models Meet Data

  • Linear Regression

  • Dimensionality Reduction with Principal Component Analysis

  • Density Estimation with Gaussian Mixture Models

  • Classification with Support Vector Machines

As of now, the fairly short book is wrapped up in near about 400 pages, so that all the topics are not covered. Also, to add to it, the PDF of this book will be freely available to the audience after publication.

For further information regarding its table of contents, notations and more, refer to the link mentioned below:

For More Information: GitHub