I think your thinking is right, machine learning is probably a good area as far as jobs go. Depending on your CS interests you'll find what areas of math they use most. AI / machine learning, learn lots of probability and statistics. High-performance, scientific computing, graphics, maybe more of a focus on linear algebra. Algorithms or theory or programming languages, lots of discrete math, logics, algebraic structures, etc. Of course it's good to get a good grasp of the basics of all these, since they're definitely not exclusive, and which you'll get from your degree, and beyond that, well explore, and enjoy!! Good luck!
It depends on what side of AI/ML you end up on, though. The research-heavy side involves lots of math and requires a PhD (or a MS + experience, in industry); the data engineering & infrastructure side is almost solely coding (and distributed systems design).
And even in machine learning, the vast majority of the work is in feature engineering, which is applying domain-specific knowledge (e.g. linguistics) to generate discriminative features.
You can only get paid for doing research on the mathematical side of ML if you work in academics or for e.g. Google. The rest will just use an off-the-shelf machine learning software.
Thanks for the comment. High performance and scientific computing jobs seem a lot rarer than AI / machine learning jobs, which is why I think the latter is a better choice for me (I have no preferences between those areas right now).
I'm also fairly interested to see how AI / machine learning develops in the future, and I think it will involve more math, so it definitely seems to be a good choice.
Comments
I think your thinking is right, machine learning is probably a good area as far as jobs go. Depending on your CS interests you'll find what areas of math they use most. AI / machine learning, learn lots of probability and statistics. High-performance, scientific computing, graphics, maybe more of a focus on linear algebra. Algorithms or theory or programming languages, lots of discrete math, logics, algebraic structures, etc. Of course it's good to get a good grasp of the basics of all these, since they're definitely not exclusive, and which you'll get from your degree, and beyond that, well explore, and enjoy!! Good luck!
It depends on what side of AI/ML you end up on, though. The research-heavy side involves lots of math and requires a PhD (or a MS + experience, in industry); the data engineering & infrastructure side is almost solely coding (and distributed systems design).
And even in machine learning, the vast majority of the work is in feature engineering, which is applying domain-specific knowledge (e.g. linguistics) to generate discriminative features.
You can only get paid for doing research on the mathematical side of ML if you work in academics or for e.g. Google. The rest will just use an off-the-shelf machine learning software.
Thanks for the comment. High performance and scientific computing jobs seem a lot rarer than AI / machine learning jobs, which is why I think the latter is a better choice for me (I have no preferences between those areas right now).
I'm also fairly interested to see how AI / machine learning develops in the future, and I think it will involve more math, so it definitely seems to be a good choice.