详见 Github

A collection of resources to learn mathematics for machine learning.

## Mathematics for Machine Learning

*by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong*

This is probably the place you want to start. Start slowly and work on some examples. Pay close attention to the notation and get comfortable with it.

Book: https://mml-book.github.io

## The Elements of Statistical Learning

*by Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie*

Machine learning deals with data and in turn uncertainty which is what statistics aims to teach. Get comfortable with topics like estimators, statistical significance, etc.

Book: https://hastie.su.domains/ElemStatLearn/

If you are interested in an introduction to statistical learning, then you might want to check out “An Introduction to Statistical Learning”

## Probability Theory: The Logic of Science

*by E. T. Jaynes*

In machine learning, we are interested in building probabilistic models and thus you will come across concepts from probability theory like conditional probability and different probability distributions.

Source: https://bayes.wustl.edu/etj/prob/book.pdf

## Probabilistic Machine Learning: An Introduction

*by Kevin Patrick Murphy*

This book contains a comprehensive overview of classical machine learning methods and the principles explaining them.

Book: https://probml.github.io/pml-book/book1.html

## Multivariate Calculus by Imperial College London

*by Dr. Sam Cooper & Dr. David Dye*

Backpropagation is a key algorithm for training deep neural nets that rely on Calculus. Get familiar with concepts like chain rule, Jacobian, gradient descent,.

Video Playlist: https://www.youtube.com/playlist?list=PLiiljHvN6z193BBzS0Ln8NnqQmzimTW23

## Mathematics for Machine Learning – Linear Algebra

*by Dr. Sam Cooper & Dr. David Dye*

Agreat companion to the previous video lectures. Neural networks perform transformations on data and you need linear algebra to get better intuitions of how that is done.

Video Playlist: https://www.youtube.com/playlist?list=PLiiljHvN6z1_o1ztXTKWPrShrMrBLo5P3

## Mathematics for Deep Learning

This reference contains some mathematical concepts to help build a better understanding of deep learning.

Chapter: https://d2l.ai/chapter_appendix-mathematics-for-deep-learning/index.html

## The Matrix Calculus You Need For Deep Learning

*by Terence Parr & Jeremy Howard*

In deep learning, you need to understand a bunch of fundamental matrix operations. If you want to dive deep into the math of matrix calculus this is your guide.

Paper: https://arxiv.org/abs/1802.01528

## Information Theory, Inference and Learning Algorithms

*by David J. C. MacKay*

When you are applying machine learning you are dealing with information processing which in essence relies on ideas from information theory such as entropy and KL Divergence,…

Book: https://www.inference.org.uk/itprnn/book.html

## Foundations of Machine Learning

*by Mehryar Mohri, Afshin Rostamizadeh & Ameet Talwalkar*

A comprehensive and accessible overview of the mathematics behind most learning algorithms (except deep learning). The appendix alone is worth a detour.

Book: https://cs.nyu.edu/~mohri/mlbook/

## Statistics and probability

*by Khan Academy*

A complete overview of statistics and probability required for machine learning.

Course: https://www.khanacademy.org/math/statistics-probability

## Linear Algebra

*by Khan Academy*

Vectors, matrics, operations on them, dot & cross product, matrix multiplication etc. is essential for the most basic understanding of ML maths.

Course: https://www.khanacademy.org/math/linear-algebra

## Calculus

*by Khan Academy*

Precalculus, Differential Calculus, Integral Calculus, Multivariate Calculus

Course: https://www.khanacademy.org/math/calculus-home

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