Mathematical Skills

A solid grasp of statistics, probability, linear algebra, and calculus is fundamental. These mathematics principles are the building blocks of machine learning algorithms, helping in understanding how models learn from data and make predictions.

A solid grasp of statistics, probability, linear algebra, and calculus is fundamental. These mathematics principles are the building blocks of machine learning algorithms, helping in understanding how models learn from data and make predictions.

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Rutika Bhoir
Grad Student at University of Massachusetts, Amherst

Okay, let’s talk about the thing nobody glamorizes enough: the math is hard. Statistics, probability, linear algebra, calculus—these aren’t just buzzwords in AI. They’re the backbone. But that doesn’t make them easy. In my first semester of grad school, I took a reinforcement learning course (shoutout to my professor Bruno Castro da Silva), and it wrecked me. The math was heavy. The pace was brutal. And now I’m in “Algorithms for Data Science” in my second semester and somehow… it’s worse?! I won’t lie—there were days I stared at equations and just felt like giving up. But here’s the thing: STEM is hard for everyone. And like Ben Cichy said in his tweet—he had a 2.4 GPA his first semester and went on to land spacecraft on Mars. Curiosity and persistence matter way more than being perfect. Math still kicks my butt regularly, but weirdly, I love it. There’s something satisfying about seeing a matrix operation finally make sense, or watching gradient descent click. You don’t have to be a math genius to be in AI. You just have to be someone who’s willing to keep going even when it’s tough. So yeah—it’s a lot. But don’t let that scare you off. Stick with it. Bit by bit, it does start making sense. I know it’s hard. But I also know you’ve got this.

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