A collection of resources to learn and review mathematics for machine learning.

by Jean Gallier and Jocelyn Quaintance

Includes mathematical concepts for machine learning and computer science.

Book: https://www.cis.upenn.edu/~jean/math-deep.pdf

by Ian Goodfellow and Yoshua Bengio and Aaron Courville

This includes the math basics for deep learning from the Deep Learning book.

Chapter: https://www.deeplearningbook.org/contents/part_basics.html

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

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

by Brent Werness, Rachel Hu et al.

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

by Benoit Liquet, Sarat Moka and Yoni Nazarathy

This book provides a complete and concise overview of the mathematical engineering of deep learning. In addition to overviewing deep learning foundations, the treatment includes convolutional neural networks, recurrent neural networks, transformers, generative adversarial networks, reinforcement learning, and multiple tricks of the trade. The focus is on the basic mathematical description of deep learning models, algorithms and methods.

Book: https://deeplearningmath.org

by Alicia A. Johnson, Miles Q. Ott, Mine Dogucu

Great online book covering Bayesian approaches.

Book: https://www.bayesrulesbook.com/index.html

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

by Gitta Kutyniok

An article summarising the importance of mathematics in deep learning research and how it’s helping to advance the field.

Paper: https://arxiv.org/pdf/2203.08890.pdf

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

by Dr. Sam Cooper & Dr. David Dye

A great 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

by Anand Avati

Lectures containing mathematical explanations to many concepts in machine learning.

Course: https://www.youtube.com/playlist?list=PLoROMvodv4rNH7qL6-efu_q2_bPuy0adh

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".

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

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

by Khan Academy

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

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

by Sheldon Axler

Slides and video lectures on the popular linear algebra book Linear Algebra Done Right.

Lecture and Slides: https://linear.axler.net/LADRvideos.html

by Khan Academy

Vectors, matrices, 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

by Khan Academy

Precalculus, Differential Calculus, Integral Calculus, Multivariate Calculus

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

This collection is far from exhaustive but it should provide a good foundation to start learning some of the mathematical concepts used in machine learning. Reach out on Twitter if you have any questions.