Learning Math for Machine Learning by Vincent Chen.
From the post:
It’s not entirely clear what level of mathematics is necessary to get started in machine learning, especially for those who didn’t study math or statistics in school.
In this piece, my goal is to suggest the mathematical background necessary to build products or conduct academic research in machine learning. These suggestions are derived from conversations with machine learning engineers, researchers, and educators, as well as my own experiences in both machine learning research and industry roles.
To frame the math prerequisites, I first propose different mindsets and strategies for approaching your math education outside of traditional classroom settings. Then, I outline the specific backgrounds necessary for different kinds of machine learning work, as these subjects range from high school-level statistics and calculus to the latest developments in probabilistic graphical models (PGMs). By the end of the post, my hope is that you’ll have a sense of the math education you’ll need to be effective in your machine learning work, whatever that may be!
…
I headlined:
…my goal is to suggest the mathematical background necessary to build products or conduct academic research in machine learning.
because the amount of math you need for machine learning depends on your use of machine learning tools.
If you intend to “build products or conduct academic research in machine learning,” then Chen’s post is as good a place to start as any. And knowing more math is always a good thing. If for no other reason than to challenge “machine learning” others try to foist off on you.
However, there are existing machine learning tools which come with their own documentation and lore about their use in a wide variety of situations.
I always applaud deeper understanding of vulnerabilities or code, but it isn’t necessary that you re-write every, most, some tools from scratch to be effective in using machine learning.
While learning the math of machine learning at your own pace, I suggest:
- Define the goal of your machine learning. Recommendation? Recognition?
- Define the subject area and likely inputs for your goal.
- Search for the use of your tool (if you already have one) and experience reports.
- Test and compare your results to industry reports in the same area.
My list assumes you already understand the goals of your client. Except in rare cases, machine learning is a means to reach those goals, not a goal itself.