Any chance you could add it in the future?
But yes, I unfortunately had to cut a probability chapter. I think someone who reads this book would have a much easier time learning probability after, and a better foundation.
So will you be following up with a prob & stats book? ;)
It saddens me that schools may be handing out CS degrees without having first required students to at least have taken linear algebra, multivariable calculus, and discrete math that covers basic counting, sets, graphs and groups. How can this be?
Probability and statistics should also be a required part of every CS program.
I would recommend a book I found that covers Statistics using Python:
Statistical Methods for Machine Learning https://machinelearningmastery.com/products/
Personally, I much prefer that book to the free "Think Stats" one available.
You can also find a book there on Linear Algebra and others related to Deep Learning, all containing real implementations with Python.
I have no affiliation with the site; these books have proven so valuable to me that I need to share them.
Anyway, the book mentioned in the OP looks like a great foundation and I picked up a copy as a refresher. I would have also liked to have seen coverage of number theory in this book, with RSA being an application, but that too would have required lengthy explanation for any useful insights to be learned.
The path to a deep understanding of inference that makes most modern ML seem straightforward goes through measure theory, Lebesgue integration, some functional analysis, a smattering of topology, and an intuition around dynamical systems. Then you go do probability and random processes, detour into game theory for a bit, learn decision theory, and at that point it all goes from mystical to bleedin' obvious.