It was a really good course, but at the time I was not in a mental space where I could get the most out of it, so I'm kind of hoping someone else can point to a solid textbook or something.
There is also a MOOC (same title, same author) on Coursera that I found interesting. It doesn't not go to the same depth as the book however. They are more complementing each other, the book for theory, the MOOC for implementation exercises.
https://github.com/ChristopheRappold/MCandPython/blob/master...
Also just to mention something else that is very interesting related with MC : "quasi-Monte Carlo techniques" (small example in https://github.com/ChristopheRappold/MCandPython/blob/master... ). For people interested in MC, just take a look to those keywords.
This article brings it up, but only as a "look at what you can do with MC methods" and not "here's this fundamental relation that's really important."
There's lots of useful understanding about importance sampling and related techniques that seem obvious when you look at it as an integration problem.