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It looks to me like it's pitched approximately at third-year undergraduates who have a couple of years of a college math background, and is meant to pull them u
by dfan 9y ago
It looks to me like it's pitched approximately at third-year undergraduates who have a couple of years of a college math background, and is meant to pull them up to a level where they can fluently work with the more advanced concepts used in machine learning.
The route for someone with no (or very little) math background is a lot longer, and I don't think this book is trying to provide it. I think that one either has to 1) bite the bullet and learn a year or two of undergrad math first, which provides the necessary foundation for this stuff, then learn this for real; or 2) be content with understanding and using machine learning at a hand-wavy level (which I am not denigrating). It might be nice to have a "hand-wavy machine learning" book around (there are certainly enough blog posts of that sort), but this isn't trying to be it.
- monotypical 9y agoIt seems to follow the 4th year/masters course of the same name taught by one of the authors (Dr Marc Deisenroth) at Imperial College London: http://www.imperial.ac.uk/computing/current-students/courses/496/ http://www.imperial.ac.uk/computing/current-students/courses... The course requires a 1st year linear algebra course and a 2nd year statistics course, which might explain why this book doesn't cover some of the basic concepts