Hi and thanks for taking the time to read my question. I have a goal to read Kevin P Murphy's Machine Learning, A probabilistic Perspective. I have only read half way through an introductory real analysis textbook (Steven L Ray's Analysis with an introduction to proof). I also own Zorich's Mathematical Analysis I and II, as well as Paul L Halmos' Finite Dimensional Vector Spaces.
My current plan is to go through the vector algebra and the real analysis books, then read through the chapters on higher dimensional derivatives/integration from Zorich's Analysis I. For anyone who has read Kevin's Machine Learning textbook, will this be a good enough background in the linear algebra/calculus required? If not, what would you recommend and why? Also, are there any other prerequisites for reading Kevin's book aside from linear algebra and calculus?
I am also considering reading the chapters on measure theory in Zorich's Analysis II, but unsure if this would be really necessary (as I am learning the measure theoretic approach to probability at University).
I would like to be as rigorous as possible (which is why I am learning calculus from an analysis book rather than a "calculus" textbook), but I am happy to leave the derivations of the probability distributions until later. It is quite difficult to offer a 100% accurate description of what exactly I am after, so I hope I have given enough detail to enable you to come up with some tips/advice.
Finally, in case it were not clear, I would prefer to learn the theory of machine learning techniques, as I can always read a more "applied" text thereafter. Thus the focus is on the theory of machine learning techniques. Please do correct me if I have made any errors.
Thanks!
