Question

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.

About me

  • 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

No correct solution

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