I had not considered the pedagogical value of starting with PAC Bayesian bounds. It will be interesting to see ML courses that take that route.
Similar issues show up in traditional statics when considering multi-hypothesis testing. I wonder if betting/gambling over hypotheses might be a more accessible way to convey these ideas to new students.
https://books.google.co.in/books/about/User_friendly_Introdu...
Free download here
https://arxiv.org/abs/2110.11216
One of the ICMLs had a nice tutorial by Langford and Banerjee on the relationship between the different style of bounds. 2003, I think.
Regular PAC falls out by taking a uniform prior over a finite hypothesis class (and then building up VC dimension if desired, but usually by this point you realise why the bounds are unlikely to be good).
On Bayesian Bounds https://dl.acm.org/doi/10.1145/1143844.1143855
Tutorial on Practical Prediction Theory for Classification https://jmlr.csail.mit.edu/papers/v6/langford05a.html
The first one is quite in the same spirit that you like.