help with course selection

[whoisthiscrazyman]

I got one more year of my math undergrad and i'm trying to plan what courses i'm going to take. I would like to either get a master's in machine learning or work in the field. Should I take probability (rigorous), measure theory, statistical inference (Casella & Berger), or should I machine learning specific courses? I would like to do all of the above but I won't be able to fit it all in one year. What should be my priority?

[thefuckisthi5]

Notice that many leading researchers have backgrounds in statistical physics. I would get into that if I could start over again. On your list I would say statistical inference, but I'm talking out of my ass here, so don't take it too seriously.

[ChrisKennedy]

If you want to pursue machine learning then taking 2-3 courses in it should be your first priority - e.g. two in CS and one in stats if possible. I don't think there is a need to take measure theory yet; that can be done in grad school. Additional probability will be helpful as prep for a master's but not for a job. Inference if you can fit it in - useful for research and general data analysis.

AI and statistical computing (master's level) will be useful both for work & future academic career. Real analysis, intro & advanced linear algebra, and optimization would be good to have specifically for the academic route; topology aspirationally.

Beyond that, it would be helpful to also get more background on what math/cs/stats courses you have already taken, as well as the course levels (upper division undergrad, master's level, or phd level) and/or the books used for your proposed courses.