MIT OCW Resources:

This repository is a curated index of the MIT OpenCourseWare (OCW) courses I used to strengthen the mathematical foundation for my PhD in Applied Mathematics.

Because I already hold a Master's degree in Mathematical Engineering, the courses listed here are mostly advanced. If you are looking for the prerequisite material, you can find those foundational courses directly on MIT OpenCourseWare.

Disclaimer: All lectures, syllabi, problem sets, and core materials referenced here are the intellectual property of the Massachusetts Institute of Technology (MIT) and their respective professors. This repository simply serves as my personal study tracker and roadmap.

📚 My Roadmap

I followed this logical progression to build up from foundational math to advanced computational methods for PDEs:

1. Linear Algebra — 18.06SC | Fall 2011 | Undergraduate | Prof. Gilbert Strang

   Note: I have literally taken this course three times simply because I love Prof. Gilbert Strang and his way of explaining things!

2. Linear Partial Differential Equations: Analysis and Numerics — 18.303 | Fall 2014 | Undergraduate | Prof. Steven G. Johnson

3. Advanced Calculus for Engineers — 18.075 | Fall 2004 | Graduate | Dr. Dionisios Margetis & Prof. John W. M. Bush

4. Advanced Partial Differential Equations with Applications — 18.306 | Graduate |Prof. Rodolfo Rosales

5. Differential Analysis II: Partial Differential Equations and Fourier Analysis — 18.156 | Spring 2016 | Graduate | Prof. Lawrence D Guth

6. Numerical Methods for Partial Differential Equations — 18.336 | Spring 2009 | Graduate | Dr. Benjamin Seibold

💻 My Tech stack

While many of the original MIT OCW course materials natively use MATLAB or Julia for computational exercises, I heavily rely on Python.