The need for remedial math seems widespread enough that even a simple Google search for'calculus and artificial intelligence' turns up a bunch of blogs and additional courses on how to understand the math underlying these assignments.

Last year, I started writing about my experiences taking courses on machine learning and artificial intelligence. One of the big, unexpected problems I ran into was calculus and linear algebra. I've found that many online courses say you don't need much mathematics fundamentals to be a programmer, but inevitably, even in beginner courses, the underlying math was important to understand what was going on. The need for remedial math seems widespread enough that even a simple Google search for'calculus and artificial intelligence' turns up a bunch of blogs and additional courses on how to understand the math underlying these assignments. After spending a lot of time online trying to sort through this haystack of do-it-yourself calculus blogs, college class PDFs, and other resources, I came away with two websites that were outstanding for teaching basic calculus and linear algebra: Khan Academy and an on-demand tutoring service called Yup.

Vincent Chen is a student at Stanford University studying Computer Science. He is also a Research Assistant at the Stanford AI Lab. 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.

While I did learn a lot of maths while doing my engineering degree, I forgot most of it by the time I wanted to get into Machine Learning. After I graduated I never really had a need for any of the maths. I did a lot of web programming which relied on logic and I can honestly say that with each system with the word'Management' in the title I lost a third of my math knowledge! I've programmed extensions for Learning Management Systems, Content Management Systems and Customer Relationship Management Systems -- I'll leave you to figure out how much math apptitude I had after working with these systems. At the moment I've got good data science skills and can use a variety of ML and DL algorithms.

Hi all - I'm seeking advice on how to best learn the math required for doing machine learning research, particularly with regard to neural nets (and other graphical models - sorry if I'm using these terms incorrectly). My background is in cognitive science, but of a particularly computational flavor, so I've been exposed to the high level ideas behind "connectionist" models, and have used them as a sort of black box in the context of comparing their performance to human behavioral data. But my undergrad coursework is conspicuously lacking in math. I recently got a job as a software engineer in a lab that works on deep learning (in NLP applications), and I want to be able to understand the math well enough to contribute to research. The lab PI and I have discussed my interest in eventually converting to a grad student, so I want to make sure my math abilities are solid as soon as I can.