Is there an equivalent of "Thomas's calculus" for Linear Algebra?

Briefly looked through and I think you're spot on.

Please feel free to throw more leads my way, thank you!

Looking for recommendations on PhD-level papers/textbooks/reading list on Machine Learning.

I want to revisit even the most "basic" of topics such as linear/logistic regression, but with better deeper understanding.

Desired outcome: able to answer questions like

• how to test for xxx assumption
• what is the implication if xxx assumption is violated (eg. heteroskedascity of error terms)

TIA!