Reading strategy for ML books
I've read all but the last one. If you are serious about computer vision you won't get much out of this reading list. However, if you are set on these books I would start with Goodfellow's Deep Learning which is fairly self-contained and the only book that covers computer vision enough to make it rewarding for that purpose. You can branch out from here and read additional material referenced in the book where you lack background.

If you are less concerned with computer vision you can start with the other books, they are often read in the following order:


An Introduction to Statistical Learning with Applications in R
The Elements of Statistical Learning
Bishop's Pattern Recognition and Machine Learning


If you finished with these you could tackle McKay which is quite dry and boring. Shai Shalev-Shwartz et al. is a nice book that covers a lot of the material in the other books but from the perspective of Empirical Risk Minimization and covers Vapnik–Chervonenkis theory. It is more theoretical and is more structured in the classical theorem-proof-corollary scheme. The perspective taken is less practical but adds to the other books. 

I don't know the book on Convex Optimization but you can probably read it in any order. If you want to read all of the books and don't care how long it takes, you can probably read that first as it sounds more foundational.