For cryptography I would recommend Ferguson, Schneier, & Kohno's Cryptography Engineering. It's aimed at engineers so it's not so much the math-oriented text that you might expect from a MIRI course list, but that's very much on purpose by the authors and in my recommendation. The principle application of cryptography to friendly AI theory is the pragmatic discipline of designing and implementing secure protocols. Most of the lessons to be learned here is not in the math, but rather the right adversarial mindset for thinking about security problems -- what Schneier calls “professional paranoia.” Imparting this mindset on new learners of the field was a driving factor for the authors in writing this textbook.

Besides, unless you are a professional cryptographer, you should not be designing your own crypto protocols. And unless you have significant peer review, you should not be using them. The key is to understand the basic fundamentals of the field, internalize the adversarial mindset, and then learn enough math (mostly group theory) to read the academic papers directly.

Do you think Causality is a superior recommendation to Probabilistic Graphical Models?