Coursework

A collection of courses I've taken at MIT, organized by subject area.

This is a subset of all the classes I have taken. I intend to upload short overview blogs of each coursework listed on this page in due course.

8.033

Relativity I

The Special Theory of Relativity. Relativistic generalizations of Electromagnetism. Introduction to General Theory of Relativity.

Physics

8.223

Classical Mechanics II

Lagrangian Formulation of Classical Mechanics.

Physics

8.044

Statistical Mechanics I

Statistical mechanics and thermodynamics.

Physics

8.05

Quantum Physics II

Linear Algebra formulation of quantum physics.

Physics

8.07

Electromagnetism II

Vector formulation of electromagnetic theory.

Physics

8.08**

Statistical Mechanics II

Advanced statistical mechanics.

Physics

8.962

General Relativity

Graduate level course on modeling spacetime as a manifold, whose curvature is informed by matter.

Physics

18.03*

Differential Equations

Various methods of solving ODEs, with a focus on linear systems. Some methods of solving PDEs.

Mathematics

18.06*

Linear Algebra

Fundamentals of linear algebra.

Mathematics

18.211**

Combinatorics I

Introduction to combinatorial mathematics.

Mathematics

6.1220

Design and Analysis of Algorithms

Algorithm design and complexity analysis.

Computer Science & Algorithms

6.3700

Introduction to Probability

Fundamental concepts in probability theory.

Statistics & Machine Learning

6.S184**

Introduction to Diffusion Models

Special topics in diffusion models.

Statistics & Machine Learning

6.3730

Applied Statistics

Statistical methods and applications.

Statistics & Machine Learning

6.7910

Statistical Learning Theory

A theoretical formulation of machine learning.

Statistics & Machine Learning

6.7800

Inference and Information

Theory behind hypothesis tests, parameter estimation, model families and their asymptotics.

Statistics & Machine Learning

6.S966

Symmetry and its Applications to Machine Learning

Quick introduction to representation theory, and using it to build symmetry-aware machine learning models.

Statistics & Machine Learning

9.521

Non-Asymptotic Statistics

Mathematical statistics on the non-asymptotic behavior of various probabilistic objects.

Statistics & Machine Learning

24.09

Minds and Machines

A Philosophical survey of the various theories of mind, ranging from Duality in 1600s to modern neurological frameworks of the mind.

Philosophy & Cognitive Science