Machine Learning Mathematics Books Pdf

My Recommendations to Learn Mathematics for Machine Learning

Here is a compilation of books, videos, and papers to get you started.

I have always emphasized on the importance of mathematics in machine learning. Here is a compilation of resources (books, videos, and papers) to get you going.

This is not an exhaustive list but I have carefully curated it based on my experience and observations.

This is a repost of my Twitter thread that you can find here . I will keep updating the list here as I come across more useful resources.

Mathematics for Machine Learning

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

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

Note: 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.

The Elements of Statistical Learning

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

Note: machine learning deals with data and in turn uncertainty which is what statistics teach. Get comfortable with topics like estimators, statistical significance,…

Probability Theory: The Logic of Science

by E. T. Jaynes

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

Note: 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.

The Matrix Calculus You Need For Deep Learning

by Terence Parr & Jeremy Howard

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

Note: 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.

Information Theory, Inference and Learning Algorithms

by David J. C. MacKay

Note: 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,…