This post is a reproduced version of the post in my Japanese blog.

For years, a lot of beginners in machine learning have asked me such as "Do I have to learn mathematics? What kind? To what extent?" and sometimes I've found it very hard to explain in a few words. Very fortunately, once I learned linear algebra and calculus when I was a student in an department of engineering so it's very familiar to me and useful for understanding theoretical aspects of machine learning.

But recently more and more beginners are rushing into machine learning or "AI" field to get more opportunity or even jobs. As far as I've seen, some of such people have never learned university-level mathematics although ML requires them. Very much unfortunately, most of them already graduated from their university years ago and they have little opportunity for learning mathematics in class. I think they need a kind of guidelines for learning mathematics for understanding machine learning.

In this post, I'd like to review a few kinds of mathematics that may be required for understanding modern machine learning, for such beginners. FYI I have one disclaimer: I'm never mathematical expert, so there might be incorrect or wrong points in terms of mathematics. If you see any points, don't hesitate to let me know!

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