To what extent is "advanced mathematics" needed/useful in A.I. research? I am currently studying mathematics. However, I don't think I want to become a professional mathematician in the future. I am thinking of applying my knowledge of mathematics to do research in artificial intelligence. However, I am not sure how many mathematics courses I should follow. (And which CS theory courses I should follow.) 

From Quora, I learned that the subjects Linear Algebra, Statistics and Convex Optimization are most relevant for Machine Learning (see this question). Someone else mentioned that learning Linear Algebra, Probability/Statistics, Calculus, Basic Algorithms and Logic are needed to study artificial intelligence (see  this  question). 

I can learn about all of these subjects during my first 1.5 years of the mathematics Bachelor at our university. 

I was wondering, though, if there are some upper-undergraduate of even graduate-level mathematics subjects that are useful or even needed to study artificial intelligence. What about ODEs, PDEs, Topology, Measure Theory, Linear Analysis, Fourier Analysis and Analysis on Manifolds? 

One book that suggests that some quite advanced mathematics is useful in the study of artificial intelligence is Pattern Theory: The Stochastic Analysis of Real-World signals by David Mumford and Agnes Desolneux (see this page). It includes chapters on Markov Chains, Piecewise Gaussian Models, Gibbs Fields, Manifolds, Lie Groups and Lie Algebras and their applications to pattern theory. To what extend is this book useful in A.I. research?
I do not want to sound condescending, but the math you are studying at the undergraduate and even graduate level courses is not advanced. It is the basics. The title of your question should be: Is "basic" math needed/useful in AI research? So, gobble up as much as you can, I have never met a computer scientist who complained about knowing too much math, although I met many who complained about not knowing enough of it. I remember helping a fellow graduate student in AI understand a page-rank-style algorithm. It was just some fairly easy linear algebra to me, but he suffered because he had no feeling for what eigenvalues and eigenvectors were about. Imagine the things AI people could do if they actually knew a lot of math!

I teach at a math department and I regularly get requests from my CS colleagues to recommend math majors for CS PhD's becase they prefer math students. You see, math is really, really hard to learn on your own, but most aspects of computer science are not. I know, I was a math major who got into a CS graduate school. Sure, I was "behind" on operating systems knowledge (despite having decent knowledge of Unix and VMS), but I was way, way ahead on "theory". It is not a symmetric situation.