Courses I've taken or am taking

1. Complex Analysis (MATH 535)
2. Linear Algebra II(MATH 425)
3. Applied Stochastic Models (Stochastic Differential Equations)
4. Advanced Statistical Theory by Ryan Martin
5. Computational Statistics by Junhui Wang (no too much theorectical foundation needed, but need some statistics common sense) Course summary,variance reduction in MC
6. Multivariate Statistics by Junhui Wang (Gaussian distribution, multiple regression, Wishart distribution, PCA, CCA, factor analysis. Can serve as a playground for linear algebra)
7. Real Analysis (after a undergraduate analysis course)
8. Topology by Alex Furman and Kevin Whyte notes filters and convergence, compactness, etc.
9. Differential manifolds I (manifolds for beginners) by Marc Culler (There is a whole series of courses in topology, manifold, geometry offered by John Lee at UWashington). Also see these course materials U of Toronto
10. Probability Theory I and II by Cheng Ouyang
11. Differential Geometry by Louis H. Kauffman (list of textbooks on the subject) Amazon book reviews
12. Functional Analysis by Jerry Bona (my way of preparing the final is to re-write my class notes)
13. Asymptotic statistics by Martin Ryan
14. Approximation algorithms by Gyorgy Turan
15. Bayesian Data Analysis by Martin Ryan
16. Structured Prediction by Brian Ziebart
17. Introduction to Algebraic Topology by Kevin Whyte
There are good applications of the subject to statistics, biology, game theory, etc., see IMA lectures UIowa applied course

In progress

To read

Online Courses

1. Convex Optimization I by Stephen P. Boyd

Self-taught Resources

1. Basic Concepts in Analysis
2. Online Texts