3 articles.
The gap between the error you measure and the error you will suffer, why picking the best of a thousand identical hypotheses makes it look 0.1149 better than chance, how capacity is counted for infinite model classes, and the theorem that equalises every learner - with the assumption that makes it true.
What changes when an agent gets noisy percepts instead of its state: the belief state that replaces it and the filtering update that maintains it, the exact reduction of a POMDP to an MDP over beliefs, the piecewise-linear convex value function that makes the reduction computable in principle, and the measured reasons it is not computable in practice - with the fixed point of belief, the alpha vectors and the value function all computed rather than asserted.
How two Markov assumptions turn an unbounded history into two small tables, the forward and backward recursions that answer every question about the present and the past, why the most likely sequence needs an algorithm of its own, and what changes when the state is a real number rather than a list.