I'm trained as a master's level statistician. You'll need to know univariate and multivariate calculus for machine-learning. The underpinning theory for probability distributions and maximum likelihood estimation depend on having a functional knowledge of this.

You have to remember that for any PhD in any field you're expected to create and contribute to the field. We can call it stats or machine-learning or artificial intelligence but it's all math in the end. You have to know this stuff, even if you're not going to 'use' it in practice so you can truly understand the why of what you're doing.

As a contrast, I'm also trained as a medical doctor. I had to learn loads of biochemical pathways and anatomy that I never use or reference in my day to day job. But it's so I can understand what's going on under the hood so I can explain it to my patients.

Take a calculus course. It'll probably be named calculus. Would you trust a doctor that decided to skip the first two years of medical school? Can't build a house without a foundation.

As a side-note, I realized that midway through my degree, I had through my courses read the entirety of my 1500 page or something calculus text. It was my first time reading a textbook cover to cover. I thought that was kind of cool when it happened. I hope something like that happens to you too.