I didn’t have much formal maths beyond 17, just a year of university statistics. I think it paid to spend some time studying linear algebra and statistics (read

I didn’t have much formal maths beyond 17, just a year of university statistics. I think it paid to spend some time studying linear algebra and statistics (reading Taleb’s books helped make it a more interesting journey), learning to read mathematical notation, and just getting kind of comfortable with the field so that you know when to ask an expert, but you don’t need to go very deep. Most AI professionals probably spend most of their time on data wrangling and engineering, not using any advanced maths. And then in order for their teams to achieve anything useful they need to work with “business” people who have domain knowledge. Also most ML algorithms I’d say can be grasped conceptually without being a big maths person, and then you have a sense of where to apply them. But over the years, you’ll pick up a lot of stuff anyway. Plus, with modern LLMs, it’s anybody’s game frankly. There might be some really tough maths on that course, but I’d argue it’s not exactly necessary. There’s undoubtedly a lot of people doing high paying “AI” related jobs whose main qualification is being able to identify a use case and coordinate a project staffed by engineers and data scientists.