[shoo]

A pathway to understanding TrueSkill could be first getting an understanding of factor graphs and Minka's expectation propagation algorithm [1][2]. I only have a superficial understanding of those things, but I think I've got an understanding of things that are close enough to give some idea of a pathway:

To understand Minka's expectation propagation algorithm you might first need to get a little intuition about assumed density filtering. One way to understand assumed density filtering could be to read a few tutorials about hidden Markov models [3] or Kalman filters and try to get a feel for why and when and how people might want to approximate posterior probability distributions. It might be hard to build enough intuition without trying to actually apply the things (implement the algorithms) or prove the theory yourself, and then try to come up with your own ideas for how to improve the algorithms.

I completely agree that there are basically infinitely more things to learn than available lifetime. It helps a lot to have a concrete application or goal in mind: then you can focus on learning the tools and theory that move you closer to the goal, rather than learning bits and pieces of unrelated knowledge that don't connect together in a useful way.

[1] https://tminka.github.io/papers/ep/roadmap.html

[2] Minka's EP slide deck from his PhD defense https://tminka.github.io/papers/ep/defense.pdf

[3] Rabiner wrote a famous HMM tutorial https://courses.physics.illinois.edu/ece417/fa2017/rabiner89...