I am hoping for a bit of guidance from experienced practitioners / academics.
I want to work through the Bishop ML book, but have minimal background.
What is the fastest way to get the pre-requisites (specific books would be appreciated)?
From searching around I found this potential self-study path:
Statistical Inference - Casella / Berger
Probability Theory and Examples - Durrett
Linear Algebra - Hoffman / Kunze
I checked these books out from the library, but they will take me over a year to work through thoroughly, so it does not seem to be practical.
I have searched around on the internet, but most of the advice doesn't list any specific books, just what subjects I should learn.
Graduated in an unrelated discipline many years ago
Willing to dedicate many hours to this (I am doing this to build a background for a degree in machine learning)
I can code pretty well due to my job