I like math, but ML is applied science, so at some point, it's an algorithm, so I don't really understand why math is really required. I'd rather read pseudo c

I like math, but ML is applied science, so at some point, it's an algorithm, so I don't really understand why math is really required. I'd rather read pseudo code than math equations. I would just assemble all the bricks myself, read the code, and ask questions later. I have problems with the scholastic method: * fill students with theory * tell them to execute the exercise * hope they understood the material * evaluate with an exam * use grades and eliminate the ones who failed This doesn't work for all students, or it works only for very very "structured" students who managed to learn this way. A lot of people learn by doing, and reading/writing math is not going to help them. That's why a lot of developers want to read the code. Math is a language, but it's not always very well defined and often, teachers have their own notation and usages, which can be frustrating. Code is well defined, and always checked and verified by a parser. Code is more reliable than mathematical notation. It's perfectly understandable for developers to refuse to use math to do machine learning, when machine learning is applied. Most of those developers want a crash course and be operational as fast as possible. The theory is not always required, unless you want to do research and go further. I have nothing against mathematics, I love maths, but for ML, you need statistics and other simple things, you don't need to go very far in the maths. Leave optimization to people who want to optimize. Do things step by step. First step is: let people understand and use machine learning.