An Incomplete List of Resources for Machine Learning

December 5, 2023

Here is a list of resources about probability, statistics, machine learning, etc. This list is not complete and is biasedMy by what I am interested in. I would highly suggest you to have a look at a few pages of these books and pick up whatever looks most appealing to you. Most of them are freely available online. If not, it should be possible to preview them on Google Books or Amazon.

I highly recommend The Book of Why by Judea Pearl. It’s about understanding cause and effect. Pearl won the Turing award (the highest honour in Computer Science) for Bayesian networks, so he knows what he’s talking about. It’s written for a general audience, so there are few equations, and lots of historical context, stories and anecdotes.

If you are interested in causal inference after reading this, I wrote a blogpost on how to get started on reading about causality.

Textbooks

My favourite is Advanced Data Analysis from an Elementary Point of View by Cosma Shalizi. I absolutely love the language and the way it is written. There is enough math to qualify it as a textbook, but it is also liberally peppered with quotes such as -

Every time someone thoughtlessly uses regression for causal inference, an angel not only loses its wings, but is cast out of Heaven and falls in extremest agony into the everlasting fire.

I think this is a good introductory book. It is entertaining to read and has many practical examples.

Bayesian Reasoning and Machine Learning by David Barber is a great, comprehensive book for Bayesian statistics.

A good introductory book to computational biology is Genomes, Networks and Evolution by Manolis Kellis.

Some other classic/trendy textbooks are -

Pattern recognition and machine learning by Christopher Bishop (also called The Bible, or The Bishop Book)

Reinforcement learning: An introduction by Sutton and Barto (also called Sutton and Barto, or The Reinforcement Learning book)

Mathematics for Machine Learning

The Deep Learning Book

Distill.pub - this is not a book, but a collection of interactive articles on deep learning

Lecture Series

Jonas Peters’ 4 part lecture on causality

David Silver’s 4 part lecture on reinforcement learning

3Blue1Brown YouTube channel - this is just an awesome math channel in general

Bonus

How much math can you understand in this video?

An Incomplete List of Resources for Machine Learning - December 5, 2023 - Tanmayee Narendra