<p>I'm majoring in math/CS, and I have to choose between a couple of classes. I'm not entirely sure which ones will benefit me more if I were to go into AI research.</p>
<p>Graph Theory vs Abstract Algebra
Diff. Equations vs Applied Complex Variables vs Non-Linear programming
Real Analysis vs Real Variables</p>
<p>Thanks!</p>
<p>None of that stuff.</p>
<p>Operations research classes, statistical modeling, bayesian statistics, regression and forecasting, computational statistics, nonparametric data analysis, machine learning, data mining, data visualization, natural language processing, etc etc. Some of those are grad level classes that you may be able to take as an undergrad. </p>
<p>You’re not going to be able to go too far in that direction in undergrad, it’s just too broad and specific at the same time. You could double major in statistics or operations research and computer science but a pure math major is not quite in the right direction. Even then, you’ll basically just be well prepared to start your PhD in CS, because it takes that much education to get into the field.</p>
<p>Thanks for the help. I understand that they’re not directly related, if at all. I’m required to take at least one class in each of the three groups. So I’m just wondering which class in each group would help me the most. If they help me equally, or none help at all, then that’s fine.</p>
<p>Graph Theory anything could be useful as many other things are done with graph theory.</p>
<p>Non-Linear programming, likewise. Very entertaining stuff. Linear Programming too.</p>
<p>Last one I’ll take the 5th.</p>
<p>Definitely go for Graph Theory and NLP. I don’t exactly know what “Real Variables” means, but Real Analysis is often a basic proof course, so it’s probably a good bet for that. Though you probably won’t make direct use of the material in the course.</p>