### Courses I've taken or am taking

- Complex Analysis (MATH 535)
- Linear Algebra II(MATH 425)
- Applied Stochastic Models (Stochastic Differential Equations)
- Advanced Statistical Theory by Ryan Martin
- Computational Statistics
by Junhui Wang
(no too much theorectical foundation needed, but need some statistics common sense)
Course summary,variance reduction in MC
- Multivariate Statistics
by Junhui Wang
(Gaussian distribution, multiple regression, Wishart distribution, PCA, CCA, factor analysis. Can serve as a playground for linear algebra)
- Real Analysis (after a undergraduate analysis course)
- Topology
by Alex Furman
and Kevin Whyte
notes
filters and convergence, compactness, etc.
- 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
- Probability Theory I and II by Cheng Ouyang
- Differential Geometry by Louis H. Kauffman (list of textbooks on the subject) Amazon book reviews
- Functional Analysis by Jerry Bona (
*my way of preparing the final is to re-write my class notes*)
- Asymptotic statistics by Martin Ryan
- Approximation algorithms by Gyorgy Turan
- Bayesian Data Analysis by Martin Ryan
- Structured Prediction by Brian Ziebart
- 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

- Convex Optimization I by Stephen P. Boyd

### Self-taught Resources

- Basic Concepts in Analysis
- Online Texts