Learning theory is a subject I am very close to, so Chapter 7 of Kornai's "Mathematical Linguistics" is one I read with great interest. The first section is a quick run-through of essential concepts from information and coding theory. The second section is an equally quick run-through of informational complexity, so-called Kolmogorov complexity. The third section is closer to my own expertise, and it discusses the three most important topics in learning theory today, namely minimum description length, identification in the limit (the Gold inductive inference paradigm), and probably approximately correct learning.
There are very few, if any, chapters or articles on formal linguistics that address all these important topics. I do feel that Kornai's chapter is too short, and fails to highlight connections between the topics considered. The subsection on identification in the limit does make what I see as the most important point about this paradigm for the linguist, which is that while Gold's theorem famously showed that no language class is identifiable which is "superfinite," a superfinite class by definition contains *all* finite languages over the vocabulary, and there is no reason to suppose that possible human languages include all (or even a single) finite language. For this reason, Gold's notorious result does not impede or even have much relevance to the formal modeling of learning human language by inductive inference. This point cannot be overemphasized, and my feeling is that research in formal modeling of human language learning was set back decades and became extremely unpopular because of widespread misunderstanding of the import of Gold's result.
For lack of time, this entry will be my last about Kornai's book for the moment. There are three further chapters which concern hidden Markov models and related things about speech and handwriting pattern recognition. A highly recommended read, I hope these posts have whet the appetite of some readers here and will send you to look at Kornai's book.