I encountered "kernels" in both a machine learning class as well as the FRM (risk management exam). In both cases, the reading given kept using the terminology without explaining it at all. I want to learn about them, but don't know where to start?
Currently at..say 2nd-3rd year undergraduate level math (linear 1, discrete/algebra 1, multivar calc, probability/stats, etc.)
Can someone give me a "learning plan" on how to work my way up to understanding it?
By "kernels", do you refer to the use of the term in models such as the SVM, or models such as the CNN? I assume the SVM. If so, I wrote a paper on the SVM for intro learners that you could probably use. Here's a shared link to the pdf on Google Drive. Note that when I wrote this paper, I was far less knowledgeable in ML than I am now, so, although the content-wise information is accurate, my model that I demonstrated at the end was a terrible example, for I wasn't that great at designing ML models at the time of writing the paper. Also, I used images of my own 2 dogs for classification, but I've removed these from this copy of the paper purely for anonymity purposes. I also have a gigantic document of resources for you to look at here.