I’m just wrapping up a PhD in ML. The notation here is unnecessarily complex IMO. Notation can make things easier, or it can make things more difficult, depending on a number of factors.
In fact, this pdf is literally the resource I've been searching for as many others are far too ambiguous and handwavey focusing more on libraries and APIs than what's going on behind the scenes.
If only there were a similar one for microeconomics and macroeconomics, I'd have my curiosity satiated.
I recall giving Mankiw a try and wished I could just find a physics-style textbook as I found it way too wordy.
E.g., micro models are just constrained optimization based on the idea of representing preference relations over abstract sets with continuous functions. So obviously, the math is then very simple. This is considered a feature. You can also use more complex math, which helps with certain proofs (especially existence and representation).
You could grab some higher level math for econ textbooks, which typically include the models as examples, where you skip over the math.
For example, for micro, you can get the following: https://press.princeton.edu/books/hardcover/9780691118673/an... I think it treats the typical micro model (up to oligopoly models) via the first 50 or so pages while explaining set theory, lattices, monotone comparative statics with Tarski/Topkis etc.
I’m glad you’re enjoying the book. The approach is ideal for a very small subset of the ML population, no doubt that was their intention. I’m just weighing in that it’s entirely possible to cover this material with rigour yet much simpler notation. Even as someone who could parse this I’d go with other options.